As enterprise’s data stores have continued to grow exponentially, managing that big data has become increasingly challenging. Organizations often find that the data they have is outdated, that it conflicts with other data in their systems or that it is just plain inaccurate.

In its 2017 global data management benchmark report data verification vendor Experian Data Quality found, “While most organizations around the globe say that data supports their business objectives, less than half of organizations globally (44%) trust their data to make important business decisions.”

The impact of that lack of trust can be significant. Organizations without accurate data can miss out on opportunities or even suffer decreases in brand value or customer satisfaction. The Experian report added, “Nearly one in two organizations globally (52%) say that a lack of confidence in data contributes to an increased threat of non-compliance and regulatory penalties, and consequently, a downturn in customer loyalty (51%).”

To avoid those consequences, organizations often turn to the discipline of data management. They set up data policies and invest in a variety of tools designed to help them handle their stores of big data.

• Big Data
• What is Big Data Management?
• Big Data Management: What You Should Know
• What is The Importance of Big Data Management?
• Big Data Technologies
• What is Big Data Concept?
• What Skills Are Needed For Big Data?
• How do Managers Use Big Data?
• Where is Big Data Stored?
• Can Big Data be Stored in SQL?
• What Are The Types of Big Data?
• Which is The Best Tool For Big Data?
• Big Data Computing
• Big Data And Business Analytics
• Big Data Management Courses
• Big Data Management Software
• Big Data Management Tools
• Big Data Management And Analytics
• Big Data Management System
• Big Data Management Jobs
• Benefits of Big Data Management
• Oracle Big Data Manager
• Data Management Techniques

Big Data

Big data is a term that describes large, hard-to-manage volumes of data – both structured and unstructured – that inundate businesses on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

What is Big Data Management?

The term “big data” usually refers to data stores characterized by the “3 Vs”: high volume, high velocity and wide variety.

Read Also: How to Become an Expert in Cyber Security

Big data management is a broad concept that encompasses the policies, procedures and technology used for the collection, storage, governance, organization, administration and delivery of large repositories of data. It can include data cleansing, migration, integration and preparation for use in reporting and analytics.

Big data management is closely related to the idea of data lifecycle management (DLM). This is a policy-based approach for determining which information should be stored where within an organization’s IT environment, as well as when data can safely be deleted.

The Data Lifecycle. Source: DataONE

Within a typical enterprise, people with many different job titles may be involved in big data management. They many include a chief data officer (CDO), chief information officer (CIO), data managers, database administrators, data architects, data modelers, data scientists, data warehouse managers, data warehouse analysts, business analysts, developers and others.

Big Data Management: What You Should Know

Big data management allows a company to understand its customers better, develop new products and make important financial decisions based on the analysis of large amounts of corporate data.

Big data management involves various processes such as the following:

• Monitoring and ensuring the availability of all big data resources through a centralized interface/dashboard.
• Performing database maintenance for better results.
• Implementing and monitoring big data analytics, big data reporting and other similar solutions.
• Ensuring the efficient design and implementation of data life-cycle processes that deliver the highest quality results.
• Ensuring the security of big data repositories and control access.
• Using techniques such as data virtualization to reduce the volume of data and improve big data operations with faster access and less complexity.
• Implementing data virtualization techniques so that a single data set can be used by multiple applications/users simultaneously.
• Ensuring that data are captured and stored from all resources as desired.

Here are five things you need to know about big data management that will help ensure consistency and trust in your analytic results.

1. Business users can do some big data management by themselves

One of the mantras of big data is availability – enabling access to numerous massive data sets in their original formats.

Today’s business users, who are more adept than their predecessors, often want to access and prepare the data in its raw format rather than having it fed to them through a chain of operational data stores, data warehouses and data marts. Business users want to scan the data sources and craft their reports and analyses around their own business needs.

Supporting business user self-service for big data has two big data management implications:

• To permit data discovery, users will have to be allowed to peruse the data independently.
• Users will need data preparation tools to assemble the information from the numerous data sets and present it for analysis.

2. It’s not your parent’s (or grandparent’s) data model

Our conventional approach to capturing and storing data for reporting and analysis centers on absorbing data into a predefined structure.

But in the big data management world, the expectation is that both structured and unstructured data sets can be ingested and stored in their original (or raw) formats, eschewing the use of predefined data models. The benefit is that different users can adapt the data sets in the ways that best suit their needs.

To reduce the risk of inconsistency and conflicting interpretations, though, this suggests the need for good practices in metadata management for big data sets.

That means solid procedures for documenting the business glossary, mapping business terms to data elements, and maintaining a collaborative environment to share interpretations and methods of manipulating data for analytical purposes.

3. Quality is in the eye of the beholder

In conventional systems, data standardization and cleansing are applied prior to storing the data in its predefined model. One of the consequences of big data is that providing the data in its original format means no cleansing or standardizations are applied when the data sets are captured.

While this provides greater freedom in the way data is used, it becomes the users’ responsibility to apply any necessary data transformations. So, as long as user transformations don’t conflict with each other, data sets may be easily used for different purposes.

This implies the need for methods to manage the different transformations and ways to ensure that they don’t conflict. Big data management must incorporate ways to capture user transformations and ensure that they are consistent and support coherent data interpretations.

4. Understanding the architecture improves performance

Big data platforms rely on commodity processing and storage nodes for parallel computation using distributed storage. Yet if you remain unfamiliar with the details of any SQL-on-Hadoop’s query optimization and execution models, you may be unpleasantly surprised by unexpectedly poor response times.

For example, complex JOINs may require that chunks of distributed data sets be broadcast to all computing nodes – causing huge amounts of data to be injected into the network and creating a significant performance bottleneck.

The upshot is that understanding how the big data architecture organizes data and how the database execution model optimizes queries will help you write data applications with reasonably high performance.

5. It’s a streaming world

In the past, much of the data that was collected and consumed for analytical purposes originated within the organization and was stored in static data repositories. Today, there is an explosion of streaming data. We have human-generated content such as data streamed from social media channels, blogs, emails, etc.

We have machine-generated data from myriad sensors, devices, meters and other Internet-connected machines. We have automatically-generated streaming content such as web event logs. All of these sources stream massive amounts of data and are prime fodder for analysis.

This is the crux of the issue. Any big data management strategy must include technology to support stream processing that scans, filters and selects the meaningful information for capture, storage and subsequent access.

What is The Importance of Big Data Management?

On social media platforms, billions of users connect daily, users share information, upload images, videos, and many more. This rising Big Data is not an overhead anymore. Companies are using this it to achieve growth and defeat their competitors.

Here arises the question of why Big Data is important for companies and What is its importance?

Big Data importance doesn’t revolve around the amount of data a company has. Its importance lies in the fact that how the company utilizes the gathered data.

Every company uses its collected data in its own way. More effectively the company uses its data, more rapidly it grows.

The companies in the present market need to collect it and analyze it because:

1. Cost Savings

Big Data tools like Apache Hadoop, Spark, etc. bring cost-saving benefits to businesses when they have to store large amounts of data. These tools help organizations in identifying more effective ways of doing business.

2. Time-Saving

Real-time in-memory analytics helps companies to collect data from various sources. Tools like Hadoop help them to analyze data immediately thus helping in making quick decisions based on the learnings.

3. Understand the market conditions

Big Data analysis helps businesses to get a better understanding of market situations.

For example, analysis of customer purchasing behavior helps companies to identify the products sold most and thus produces those products accordingly. This helps companies to get ahead of their competitors.

4. Social Media Listening

Companies can perform sentiment analysis using Big Data tools. These enable them to get feedback about their company, that is, who is saying what about the company.

Companies can use Big data tools to improve their online presence.

5. Boost Customer Acquisition and Retention

Customers are a vital asset on which any business depends on. No single business can achieve its success without building a robust customer base. But even with a solid customer base, the companies can’t ignore the competition in the market.

If we don’t know what our customers want then it will degrade companies’ success. It will result in the loss of clientele which creates an adverse effect on business growth.

Big data analytics helps businesses to identify customer related trends and patterns. Customer behavior analysis leads to a profitable business.

6. Solve Advertisers Problem and Offer Marketing Insights

Big data analytics shapes all business operations. It enables companies to fulfill customer expectations. Big data analytics helps in changing the company’s product line. It ensures powerful marketing campaigns.

7. The driver of Innovations and Product Development

Big data makes companies capable to innovate and redevelop their products.

Big Data Technologies

Let’s discuss the leading-edge technologies (in no particular order) that influence the market and IT industries in recent time;

1. Artificial Intelligence

A broad bandwidth of computer science that deals in designing smart machines capable of accomplishing various tasks that typically demand human intelligence is known as Artificial Intelligence.

From SIRI to self-driving car, AI is developing very swiftly, on being an interdisciplinary branch of science, it takes many approaches like augmented machine learning and deep learning into account to make a remarkable shift in almost every tech industry.

The excellent aspect of AI is the strength to intellectualize and make decisions that can provide a plausible likelihood in achieving a definite goal. AI is evolving consistently to make benefits in various industries. For example, AI can be used for drug treatment, healing patients, and conducting surgery in OT. 

2. NoSQL Database

NoSQL incorporates a broad range of separate database technologies that are developing to design modern applications. It depicts a non SQL  or nonrelational database that delivers a method for accumulation and retrieval of data. They are deployed in real-time web applications and big data analytics.

It stores unstructured data and delivers faster performance, and proffers flexibility while dealing with varieties of datatypes at a huge scale. Examples included MongoDB, Redis, and Cassandra.

It covers the integrity of design, easier horizontal scaling to an array of devices and ease control over opportunities. It uses data structures that are different from those accounted by default in relational databases, it makes computations quicker in NoSQL. For example, companies like Facebook, Google and Twitter store terabytes of user data every single day.

3. R Programming

R is the programming language and an open-source project. It is a free software highly used for statistical computing, visualization, unified developing environments like Eclipse and Visual Studio assistance communication. 

Expert says it has graced the most prominent language across the world. Along with it, being used by data miners and statisticians, it is widely implemented for designing statistical software and mainly in data analytics.

4. Data Lakes

Data Lakes refers to a consolidated repository to stockpile all formats of data in terms of structured and unstructured data at any scale. 

In the process of data accumulation, data can be saved as it is, without transforming it into structured data and executing numerous kinds of data analytics from dashboard and data visualization to big data transformation, real-time analytics, and machine learning for better business interferences.

Organizations that use data lakes will be able to defeat their peers, new types of analytics can be conducted such as machine learning across new sources of log files, data from social media and click-streams and even IoT devices freeze in data lakes. 

It helps organizations to know and respond to better opportunities for faster business growth by bringing and engaging customers, sustaining productivity, maintaining devices actively, and taking acquainted decisions.

5. Predictive Analytics

A subpart of big data analytics, it endeavors to predict future behavior via prior data. It works using machine learning technologies, data mining and statistical modeling and some mathematical models to forecast future events.  

The science of predictive analytics generates upcoming inferences with a compelling degree of precision. With the tools and models of predictive analytics, any firm deploys prior and latest data to drag out trends and behaviors that could occur at a particular time.

For example, to explore the relationships among various trending parameters. Such models are designed to assess the pledge or risk delivered by a specific set of possibilities. 

6. Apache Spark

With in-built features for streaming, SQL, machine learning and graph processing support, Apache Spark earns the cite as the speedest and common generator for big data transformation. It supports major languages of big data comprising Python, R, Scala, and Java. 

The Hadoop was introduced due to spark, concerning the main objective with data processing is speed. It lessens the waiting time between interrogating and program execution timing. The spark is used within Hadoop mainly for storage and processing. It is a hundred times faster than MapReduce.

7. Prescriptive Analytics

Prescriptive Analytics gives guidance to companies about what they could do when to achieve aspired outcomes. For example, it can give notice to a company that the borderline of a product is expecting to decrease, then prescriptive analytics can assist in investigating various factors in response to market changes and predict the most favorable outcomes.

Where it relates both descriptive and predictive analytics but focuses on valuable insights over data monitoring and give the best solution for customer satisfaction, business profits, and operational efficiency. 

8. In-memory Database

The in-memory database(IMDB) is stored in the main memory of the computer (RAM) and controlled by the in-memory database management system. In prior, conventional databases are stored on disk drives. 

If you consider, conventional disk-based databases are configured with the attention of the block-adapt machines at which data is written and read.Instead, When one part of the database refers to another part, it feels the necessity of different blocks to be read on the disk. This is a non-issue with an in-memory database where interlinked connections of the databases are monitored using direct indicators.  

In-memory databases are built in order to achieve minimum time by omitting the requirements to access disks. But, as all data is collected and controlled in the main memory completely, there are high chances of losing the data upon a process or server failure.

9. Blockchain

Blockchain is the assigned database technology that carries Bitcoin digital currency with a unique feature of secured data, once it gets written it never be deleted or changed later on the fact. 

It is a highly secure ecosystem and an amazing choice for various applications of big data in industries of banking, finance, insurance, healthcare, retailing, etc. 

Blockchain technology is still in the process of development, however, many merchants of various organizations like AWS, IBM, Microsoft including startups have tried multiple experiments to introduce the possible solutions in building blockchain technology.

10. Hadoop Ecosystem

The Hadoop ecosystem comprises a platform that assists in resolving the challenges surrounding big data. It incorporates a variety of varied components and services namely ingesting, storing, analyzing, and maintaining inside it. 

Majority services prevalent in the Hadoop ecosystem are to complement its various components which include HDFS, YARN, MapReduce and Common. 

Hadoop ecosystem comprises both Apache Open Source projects and other wide variety of commercial tools and solutions. A few of the well known open source examples include Spark, Hive, Pig, Sqoop and Oozie.   

What is Big Data Concept?

The definition of big data is hidden in the dimensions of the data. Data sets are considered “big data” if they have a high degree of the following three distinct dimensions: volume, velocity, and variety.

Value and veracity are two other “V” dimensions that have been added to the big data literature in the recent years. Additional Vs are frequently proposed, but these five Vs are widely accepted by the community and can be described as follows:

• Velocity: the speed at which the data is been generated
• Volume: the amount of the data that is been generated
• Variety: the diversity or different types of the data
• Value: the worth of the data or the value it has
• Veracity: the quality, accuracy, or trustworthiness of the data

Large volumes of data are generally available in either structured or unstructured formats. Structured data can be generated by machines or humans, has a specific schema or model, and is usually stored in databases. Structured data is organized around schemas with clearly defined data types.

Numbers, date time, and strings are a few examples of structured data that may be stored in database columns. Alternatively, unstructured data does not have a predefined schema or model. Text files, log files, social media posts, mobile data, and media are all examples of unstructured data.

What Skills Are Needed For Big Data?

There has been tremendous growth in the tools and techniques around Big Data and other related fields. Big Data has become the answer to using and analysing real-time data. In today’s competitive business work, no company can survive without Big Data.

1. Analytical Skills

Analytical skills are one of the most prominent Big Data Skills required to become the right expert in Big Data. To Understand the complex data, One should have useful mathematics and specific science skills in Big Data. Analytics tools in Big Data can help one to learn the analytical skills required to solve the problem in Big Data.

2. Data Visualization Skills

An individual who wants to become a Big Data professional should work on their Data Visualization Skills. Data has to be adequately presented to convey the specific message. This makes visualization skills are essential in this area.

One can start by learning the Data Visualization options in the Big Data Tools and software to improve their Data Visualization skills. It will also help them to increase their imagination and creativity, which is a handy skill in the Big Data field. The ability to interpret the data visually is a must for data professionals.

3. Familiarity with Business Domain and Big Data Tools

Insights from massive datasets are derived and analyzed by using Big data tools. To understand the data in a better way by Big Data professionals, they will need to become more familiar with the business domain, especially with the business domain of the data they are working on.

4. Skills of Programming

Having knowledge and expertise in Scala, C, Python, Java and many more programming languages are added advantages to Big Data Professional. There is a high demand for programmers who are experienced in Data analytics.

To become an excellent Big Data Professional, one should also have good knowledge of fundamentals of Algorithms, Data Structures and Object-Oriented Languages. In Big Data Market, a professional should be able to conduct and code Quantitative and Statistical Analysis.

One should also have a sound knowledge of mathematics and logical thinking. Big Data Professional should have familiarity with sorting of data types, algorithms and many more. Database skills are required to deal with a significantly massive volume of data. One will grow very far if they have an excellent technical and analytical perspective.

5. Problem Solving Skills

The ability to solve a problem can go a long way in the field of Big Data. Big Data is considered to be a problem because of its unstructured data in nature. The one who has an interest in solving problems is the best person to work in this field of Big Data.

Their creativity will help them to come out with a better solution to a problem. Knowledge and skills are only good up to a limit. Creativity and problem-solving skills are even more essential to become a competent professional in Big Data.

6. SQL – Structured Query Language

In this era of Big Data, SQL work like a base. Structured Query Language is a data centred language. It will be beneficial for a programmer while working on Big data technologies such as NoSQL to know SQL.

7. Skills of Data Mining

Experienced Data mining professionals are in high demand. One should gain skills and experiences in technologies and tools of data mining to grow in their careers. Professionals should develop most-sought data mining skills by learning from top data mining tools such as KNIME, Apache Mahout, Rapid Miner and many more.

8. Familiarity with Technologies

Professionals of Big Data Field should be familiar with a range of technologies and tools that are used by the Big Data Industry. Big Data tools help in conducting research analysis and to conclude.

It is always better to work with a maximum number of big data tools and technologies such as Scala, Hadoop, Linux, MatLab, R, SAS, SQL, Excel, SPSS and many more. There is a higher demand for professional have excellent skills and knowledge in programming and statistics.

9. Familiarity With Public Cloud and Hybrid Clouds

Most Big Data teams will use a cloud set up to store data and ensure the high availability of Data. organisations prefer cloud storage as it is cheaper to store large volumes of data when compared to building an in-house storage infrastructures. Many organizations even have a hybrid cloud implementation where in data can be stored in-house or on public cloud as per the requirements and organisation policies.

Some of the public clouds that one must know are Amazon Web Services (AWS), Microsoft Azure, Alibaba Cloud etc. The in-house cloud technologies include OpenStack, Vagrant, Openshift, Docker, Kubernetes etc.

10. Skills from Hands-on experience

An aspiring Big Data Professional should gain hands-on experience to learn the Big data tools. One can also go for short-term courses to learn the technology faster. If one has good knowledge about newer technologies, then it will help them in understanding the data better by using modern tools. Their interaction with the data will improve give them an edge over the others by bringing out better results.

How do Managers Use Big Data?

Here are five ways management teams are applying data analytics to cultivate employee development and create high-performing organizations.

Measuring Performance

Organizations can use analytics tools to establish employee performance benchmarks, and then coach existing and incoming employees to understand those qualities and their impact. Deloitte, along with other companies, analyzes human performance data, travel data and billing hours, to help individuals boost their professional performance as well as their wellness and energy.

Organizations can even use data gathered from top-performing teams or individual employees as a means to understanding effective processes and set standard benchmarks for other groups in the organization to follow.

Informing Promotion and Salary Decisions

A major demotivator for many high-performing employees is watching under-performing peers receive promotions. There can be several factors that lead to this, but human bias and nepotism can often play a part. Taking a data-based approach can help organizational leaders watch the rate at which employees are receiving promotions and raises and what key factors drive these decisions.

For example, a new employee may have just delivered an outstanding sales performance, but a longer-tenured peer may have consistently provided quality performance over time. Which performance metric carries more weight, and over what timeframe is performance measured? Should tenure be a factor at all?

Gathering and using more types and sources of data and using it to train artificial intelligence algorithms can then support managers in making less-biased decisions and ensure performance-generated data is a larger part of the equation.

Understanding Attrition and Increasing Retention

Performance-based analytics can also be applied to predict which employees might be more prone to leave, while also telling a story about what factors contribute to attrition. Money may be less of a factor than the quality of managers and supervisors, according to management consulting firm McKinsey & Co.

For example, McKinsey cites a case study of a major U.S. insurance company that implemented a bonus program in an effort to retain employees but saw little success.

Then, the company began to apply data analytics to understand at-risk workers, and they uncovered a trend: people who were on smaller teams, went longer between promotions, and who reported to lower-performing managers were all more likely to leave. Instead of pouring money into these employees, the company began pouring resources into making stronger managers.

Organizations can also glean data on their turnover rate (both voluntary and involuntary attrition divided by average headcount) to understand trends and address sudden spikes. For example, a surge in involuntary attrition may be an indication the recruiting and training process needs a review; an uptick in voluntary attrition may require deeper dives into specific departments or managers.

Examining Employee Engagement

A crucial metric for any HR department is employee engagement. This data is typically gathered via employee engagement surveys that are conducted by outsourced survey providers (i.e. Gallup). However, more organizations are seeing the benefit of bringing this in house to their HR departments for both faster results and to maintain the ownership of their employees’ data.

Instead of the extensive surveys that many employees dread (and some don’t even fill out), in house HR departments can use brief, small surveys to regularly monitor engagement, and with the help of AI tools, gain immediate data insights.

Another tool that is both an additional source of employee data and improved engagement is gamification. GamEffective, a company that designs gamification apps for businesses, offers one version where employees can place bets about how their day will go, based off their goals for the day.

This can increase not only the employee’s engagement but also motivate the employee to meet their individual and team goals, as organizations can pick specific KPIs to measure within the app.

Measuring Employee Development and Learning Outcomes

A vibrant training program can benefit organizations with a more productive workforce and improved retention. Rather than ask employees a few static questions at the completion of training, organizations can shift focus from satisfaction with the training to comprehension of the program, tracking the employee’s actual progress throughout the training.

Companies can go one step further by applying predictive analytics to customize training content that better meets employee learning styles at an individual level. At an organizational level, predictive analytics can assess weak points in the training (like when employee engagement dips).

Ultimately, this data can analyze patterns that make a training successful and direct companies to improve content in the right places.

Where is Big Data Stored?

On-Premises

Considered to be the original data storage method, an on-premises data solution typically involves servers that are owned and managed by the organization itself. For larger companies, these servers could be located in a private data center facility, but in many cases, they consist of a handful of machines located in an office’s dedicated data room (or in some cases, “closet”).

Whatever form it takes, the defining aspect of an on-premises solution is that the data’s owner takes full responsibility for building and overseeing the IT infrastructure that stores it. This deployment provides the greatest amount of control an organization can have over its network and data, but at the not insignificant cost of having to manage every aspect of it.

Outdated equipment needs to be replaced, software needs to be patched and updated, and access protocols need to be strictly regulated. For many companies, full control over data and network architecture isn’t worth the expense of setting up and operating an on-premises solution.

Colocation

While many organizations like the idea of storing data on equipment that they own and control, they don’t want to deal with the ongoing hassle of managing that equipment. Power and cooling needs can be difficult to accommodate on a regular basis, and implementing new services or features into an IT infrastructure can be challenging and time-consuming if they’re handled internally.

By colocating equipment off-premises with a data center, companies can gain the benefits of a data center’s versatility and services while still retaining complete control over their data. Rather than dealing with variable operating costs, colocation customers benefit from predictable pricing for power and cooling.

The connectivity options of data centers allow them to easily incorporate new features into their network infrastructure while the robust security and compliance protocols of a data center environment provide protections that might be more difficult for a company to implement in-house.

When remote hands support is added to the mix to address a company’s IT needs 24x7x365, colocation offers an outstanding business data storage method for many companies.

Cloud Storage

For many small to medium-sized companies, there may not be much sense in investing in expensive hardware for storing data. Migrating the whole of their data operations to a public cloud provider, whether through a lift and shift strategy or a more specialized migration, can deliver tremendous versatility and other benefits.

Public cloud solutions are usually quite scalable, making it easy to provision more storage or computing resources as they’re needed. The easy access to the cloud also allows employees to utilize data from almost anywhere, which is a huge benefit for organizations with remote workforces.

Public cloud architectures also empower edge computing strategies used by companies in the Internet of Things (IoT) market, helping them to extend their network reach into otherwise difficult to access areas and minimize latency.

Cloud storage solutions aren’t without drawbacks, however. While public clouds take security seriously, the open nature of the environment makes it difficult to protect sensitive data from unauthorized access.

For companies that can’t afford to take risks, private cloud deployments implemented through a virtualized infrastructure offer much greater levels of security, especially when coupled with encryption protocols. In many ways, private clouds are a form of colocation, only no hardware is involved.

Virtualized servers can offer companies all the benefits of physical equipment while being much easier to maintain. New approaches to network architecture, such as hybrid and multi-clouds, can store sensitive data in secure private clouds while still taking advantage of the computing power of public cloud services.

Can Big Data be Stored in SQL?

A specific SQL product has a performance level and may or may not have problems with supporting big data. For example, some SQL products have a very small footprint making them suitable to run on small devices, such as SQLite.

Such SQL systems are definitely not built for big data systems. But on the other end of the scale there are SQL systems that are developed for storing and analyzing big data, such as Amazon RedShift, Exasol, HP/Vertica, IBM PureData Systems for Analytics (Netezza), Kognitio, and the Teradata databases and Teradata Aster.

SQL systems have proven that they can be used in big data systems. For example, the amount of data eBay processes every day adds up to an astonishing 50 petabytes. And they use Teradata. There are many more organizations that use SQL products to run big data systems.

Evidently, there are use cases of big data for which specific SQL products are not the right data storage technology and where, for example, Hadoop or NoSQL products make more sense. But the opposite is true as well, for some big data use cases a specific SQL product is preferred.

The point is that you can’t make those types of generalized remarks about SQL. Like you can’t say that movies are too long or that books are difficult to read. You have to be specific, you have to indicate to which products you refer. Not all SQL products are created equal.

And don’t forget that more and more massive big data systems developed on Hadoop use some SQL-on-Hadoop engine, such as Apache Hive or Impala. Isn’t a SQL-on-Hadoop engine running on Hadoop a SQL product?

A study by TDWI has indicated that 28% of the organizations already use Hadoop and 22% of all the organizations use SQL-on-Hadoop, and 36% of the organizations plan to use Hadoop within three years and approximately the same percentage plans on using a SQL-on-Hadoop engine.

In other words, due to these SQL-on-Hadoop engines, more and more SQL products are used for running big data systems.

What Are The Types of Big Data?

Now that we are on track with what is big data, let’s have a look at the types of big data:

Structured

Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms.

For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner. 

Unstructured

Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data.

Semi-structured

Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data.

To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data.

Which is The Best Tool For Big Data?

Big Data technologies, such as Apache Spark and Cassandra are in high demand. Companies are looking for professionals who are skilled in using them to make the most out of the data generated within the organization.

These data tools help in handling huge data sets and identifying patterns and trends within them. So, if you are planning to get into the Big Data industry, you have to equip yourself with these tools. 

1. Apache Storm

Apache Storm is a real-time distributed tool for processing data streams. It is written in Java and Clojure, and can be integrated with any programming language. The software was developed by Nathan Marz and was later acquired by Twitter in 2011. The basic features of Storm are as follows:

• Has massive scalability
• It can process over a million jobs on the node within fractions of seconds
• Real-time data processing
• Storm topology runs until the user shuts it down or an unexpected technical failure occurs
• It guarantees the processing of every tuple
• It can run on JVM (Java Virtual Machine)
• Apache Storm supports (DAG) Direct Acrylic Graph topology
• Being open-source, flexible and robust, it can be used by medium and large-scale organizations
• It has low latency. Performs end-to-end delivery response and data refresh in seconds, depending on the data problem
• Storm guarantees data processing even if the messages are lost or nodes of the cluster die   

The Apache Storm topologies are like a MapReduce job. But, here the data is processed in real-time instead of batch processing in Apache Spark. 

Storm UI daemon offers you a REST API through which you can do the following:

• Interact with the Storm cluster and obtain metrics data
• Start/stop topologies and configure information
• Even if a failure happens, each node is processed at least once

All this make Storm one of the leading Big Data technologies at present.

2. MongoDB

This is an open-source NoSQL database that is an advanced alternative to modern databases. It is a document-oriented database used for storing large volumes of data. Instead of rows and columns used in traditional databases, you will make use of documents and collections.

Documents consist of key-value pairs and the collections have function and document sets. MongoDB is ideal for companies who need to take quick decisions and want to work with real-time data. The Big Data technology is commonly used for storing data obtained from mobile applications, product catalogues and content management systems.

Some of the most popular reasons for getting started with MongoDB are:

• As it stores data in documents, it is very flexible and can be easily adapted by companies
• It supports many ad-hoc queries, such as searching by a field name, regular expressions and range queries. You can execute queries for returning fields in a document
• All fields of a MongoDB document can be indexed for enhancing the quality of searches
• It is great at load balancing as it splits data across MongoDB instances. The technology can run on several servers, and also duplicates data for load balancing in case a technical failure occurs
• You can store data of any type, such as integer, strings, Booleans, arrays and objects
• As this technology uses dynamic schemas, you can store and prepare data quickly, thus saving cost.

3. Cassandra

Cassandra is a distributed database management system that is used for handling large volumes of data across several servers. This is one of the most popular Big Data technologies which is preferred for processing structured data sets. It was first developed by Facebook as a NoSQL solution. It is now used by corporate giants, such as Netflix, Twitter and Cisco.

The most exciting features of Cassandra include:

• It provides an easy to use query language, so it will be hassle-free if you want to transition from a relational database to Cassandra
• Its Masterclass architecture allows data to be read and written on any node
• Data is replicated on different nodes, so there is no single point of failure. Even if a node fails to work, data stored on other nodes will be available for use
• Data can also be replicated across multiple data centres. So, if data is lost or damaged in one data centre, it can be retrieved from other data centres
• It has built-in security features, such as restore mechanisms and data backup
• This tool allows the detection and recovery of failed nodes

Cassandra is now widely used in IoT real world applications where huge streams of data are coming from devices and sensors. It is widely used for social media analytics and while handling customer data.

4. Cloudera

Cloudera is one of the fastest and most secure Big Data technologies out there right now. It was initially developed as an open-source Apache Hadoop distribution that was aimed at enterprise-class deployments. This scalable platform allows you to get data from any environment very easily.

The best features why choosing Cloudera will be great for your project are:

• Offers real-time insights for data monitoring and detection
• You can deploy Cloudera Enterprise across various cloud platforms, such as AWS, Google Cloud and Microsoft Azure
• Cloudera has the capability of developing and training data models
• You can spin or terminate data clusters. This allows you to pay for only what you need and when you require it
• Offers an enterprise-level hybrid cloud solution

Cloudera offers software, support and service in five bundles that are available across multiple cloud providers and on-premise:

• Cloudera Enterprise Data Hub
• Cloudera Analytic DB
• Cloudera Operational DB
• Cloudera Data Science and Engineering 
• Cloudera Essentials

5. OpenRefine

OpenRefine is a powerful Big Data tool that is used for cleaning data and converting it into different formats. You can explore huge data sets using this tool comfortably. The prominent features of this tool are:

• You can extend your data set to various web services
• Import data in different formats 
• Handle cells with multiple data values and perform cell transformations
• You can use Refine Expression Language to perform advanced data operations
• The tool allows you to explore huge data sets easily within a matter of seconds

Big Data Computing

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few.

Product development	Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.
Predictive maintenance	Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.
Customer experience	The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
Fraud and compliance	When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.
Machine learning	Machine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.
Operational efficiency	Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.
Drive innovation	Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Big Data And Business Analytics

Big data analytics is the often complex process of examining big data to uncover information — such as hidden patterns, correlations, market trends and customer preferences — that can help organizations make informed business decisions.

On a broad scale, data analytics technologies and techniques give organizations a way to analyze data sets and gather new information. Business intelligence (BI) queries answer basic questions about business operations and performance.

Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by analytics systems.

Why is big data analytics important?

Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.

How does big data analytics work?

Data analysts, data scientists, predictive modelers, statisticians and other analytics professionals collect, process, clean and analyze growing volumes of structured transaction data as well as other forms of data not used by conventional BI and analytics programs.

Here is an overview of the four steps of the data preparation process:

1. Data professionals collect data from a variety of different sources. Often, it is a mix of semi-structured and unstructured data. While each organization will use different data streams, some common sources include:

• internet clickstream data;
• web server logs;
• cloud applications;
• mobile applications;
• social media content;
• text from customer emails and survey responses;
• mobile phone records; and
• machine data captured by sensors connected to the internet of things (IoT).

2. Data is processed. After data is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data properly for analytical queries. Thorough data processing makes for higher performance from analytical queries.
3. Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.
4. The collected, processed and cleaned data is analyzed with analytics software. This includes tools for:

• data mining, which sifts through data sets in search of patterns and relationships
• predictive analytics, which builds models to forecast customer behavior and other future developments
• machine learning, which taps algorithms to analyze large data sets
• deep learning, which is a more advanced offshoot of machine learning
• text mining and statistical analysis software
• artificial intelligence (AI)
• mainstream business intelligence software
• data visualization tools

Big Data Management Courses

Big Data refers to the analysis of large data sets to find trends, correlations or other insights not visible with smaller data sets or traditional processing methods. The exponential growth of internet-connected devices and sensors is a major contributor to the massive data and the storage, processing and analysis can require hundreds or thousands of computers.

An example of big data in use is in the development of autonomous vehicle. The sensors on self-driving vehicles are capturing millions of data points that can be analyzed to help improve performance and avoid accidents.

Learn the fundamentals of big data with free online courses designed to introduce you to this in-demand field and teach you how to design and implement big data analytics solutions. Learn key tools and systems for working with big data such as Azure, Hadoop and Spark and learn how to implement NoSQL data storage and processing solutions.

For an advanced certificate in big data, consider the 15-course Microsoft Professional Program in Big Data on edx. This multi-unit program is designed to get you on a path to a new career. Learn how to process real-time data streams and implement real-time big data analytics solutions.

Students will also learn how to use Spark to implement predictive analytics solutions, one of the key benefits of big data. Get started with the self-paced orientation course that covers data formats, big data technologies and the basics of databases.

Big Data Management Software

we outline the top 5 best Big Data software with their key features to boost your interest in big data and develop your Big Data project effortlessly.

1. Hadoop

Apache Hadoop is one of the most prominent tools. This open-source framework permits reliable distributed processing of a large volume of data in a dataset across clusters of computers. Basically, it is designed for scaling up single servers to multiple servers. It can identify and handle the failures at the application layer. Several organizations use Hadoop for their research and production purposes.

2. Quoble

Quoble is the cloud-native data platform that develops a machine learning model at an enterprise scale. The vision of this tool is to focus on data activation. It permits to process of all types of datasets to extract insights and build artificial intelligence-based applications.

3. HPCC

LexisNexis Risk Solution develops HPCC. This open-source tool provides a single platform, single architecture for data processing. It is easy to learn, update, and program. Additionally, easy to integrate data and manage clusters.

4. Cassandra

Do you need a big data tool that will you provide scalability and high availability as well as excellent performance? Then, Apache Cassandra is the best choice for you. This tool is a free, open-source, NoSQL distributed database management system. For its distributed infrastructure, Cassandra can handle a high volume of unstructured data across commodity servers.

5. MongoDB

This Database Management tool, MongoDB, is a cross-platform document database that provides some facilities for querying and indexing, such as high performance, high availability, and scalability. MongoDB Inc. develops this tool and is licensed beneath the SSPL (Server Side Public License). It works on the idea of collection and document.

Big Data Management Tools

Data Management is as successful as the tools used to store, analyze, process, and discover value in an organization’s data. In essence, these tools are heterogeneous multi-platform management systems that harmonize data.

The most widely used data management tools belong to the industry’s biggest software groups whose experience guarantees a high degree of performance, security, efficiency, effectiveness, elimination of data redundancy, and privacy that is necessary for companies that are leaving the entire organization’s information in the care of external vendors.

Here’s a list of the most prominent data management tools on the market.

1. Oracle Data Management Suite

Oracle Data Management Suite: Comprehensive platform that delivers a suite of solutions that enable users to build, deploy, and manage data-driven projects by delivering consolidated, consistent, and authoritative master data across an enterprise and distributes this information to all operational and analytical applications. It enables data governance and quality, policy compliance, repeatable business processes, cross-functional collaboration, and change awareness throughout the enterprise.

2. SAP Data Management

SAP Data Management: Integrated technology platform that uses a single point to access all data, whether transactional, analytical, structured, or unstructured, across on-premise and cloud-based solutions.

It provides access to metadata management tools to enable an intelligent data management process by taking advantage of the cloud benefits, which include low cost of ownership, elasticity, serverless principles, high availability, resilience, and autonomous behavior.

3. IBM Infosphere Master Data Management Server

IBM Infosphere Master Data Management Server: A comprehensive tool that helps manage enterprise data to present it into a single trusted view and deliver analytic capabilities. It includes a security system, transaction control, multi-domain support, event management and data quality analysis.

It manages all aspects of critical enterprise data, regardless of system or model, and delivers actionable insights, instant business value alignment, and compliance with data governance, rules and policies across an enterprise. IBM Infosphere orchestrates data throughout the complete information lifecycle.

4. Microsoft Master Data Services

Microsoft Master Data Services: Platform that includes a suite of services that enables users to manage a master set of an organization’s data. Data can be organized in models, it can be updated by creating rules, and it can include access controls to authorize who updates the data.

It enables users to develop MDM solutions that are built on top of an SQL Server database technology for back-end processing. It provides service-oriented architecture endpoints using Windows Communication Foundation (WCF) and it implements a hub architecture using MDS to create centralized and synchronized data sources to reduce data redundancies across systems.

• Microsoft Azure Data Factory: It is a hybrid data integration service that simplifies ETL at scale and is specifically designed for all data integration needs and skill levels. With its rich visual environment, users can easily construct ETL and ETL processes in a code-free fashion by integrating data sources from more than 80 natively-built and maintenance-free connectors.
• Microsoft SQL Server SSIS: Microsoft SQL Server Integration Services (SSIS) is a platform for building enterprise-level data integration and data transformations solutions. It solves complex business problems by copying or downloading files, loading data warehouses, cleaning and mining data, and managing SQL Server objects and data. Additionally, it extracts and transforms data from a wide variety of sources such as XML data files, flat files, and relational data sources, and then loads the data into one or more destinations. The platform includes a rich set of built-in tasks and transformations, graphical tools for building packages, and the Integration Services Catalog database to store, run, and manage packages. Last but not least, it allows users to leverage the graphical Integration Services tools to create solutions without writing a single line of code.
• Microsoft Power BI: Business analytics service that delivers insights to enable fast, informed decisions. It helps transform data into compelling visuals that can be shared on any device to visually explore and analyze data, on-premises and in the cloud, all in one view. Additionally, it enables collaboration through customized dashboards and interactive reports, and it scales easily with built-in governance and security.

5. Dell Boomi

Dell Boomi: Enterprise-grade platform that is unified and versatile, leveraging all the advantages of the cloud. The platform is designed to provide ease of use and high productivity by:

• Connecting all applications and data sources across a hybrid IT landscape.
• Synchronizing and enriching data through a centralized data hub.
• Achieving interoperability between internal systems and external partners.
• Exposing underlying data as APIs to deliver scalable and secure, real-time interactions.
• Transforming manual processes into automated processes with flexible business logic and workflow capabilities. 

6. Talend

Talend: Single, open platform for data integration, data management, enterprise application integration, data quality, cloud storage, and Big Data across cloud and on-premise environments. It helps transform data into business insights to help companies make real-time decisions and become data-driven.

7. Tableau

Tableau: Interactive data visualization solution that helps users see and understand data. It helps simplify raw data into an easily understandable format for smart data analysis. Visualizations are created in the form of dashboards and worksheets through its key features that include data blending, real-time analysis, and data collaboration.

8. Amazon Web Services – Data Lakes and Analytics

Amazon Web Services – Data Lakes and Analytics: Integrated suite of services that provide the necessary solutions to build and manage a data lake for analytics. AWS-powered data lakes are capable of handling the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights. AWS provides a comprehensive set of services to move, store, and analyze data.

9. Google Cloud – Big Data analytics

Google Cloud – Big Data analytics: Solution platform that offers a broad set of tools for cloud-based data management, as well as a workflow manager to tie components together such as BigQuery for tabular data storage, Cloud BigTable for NoSQL database-style storage, Cloud Pub and Cloud Data Transfer for data intake, ML Engine for advanced analysis via machine learning and artificial intelligence, Data Studio for GUI-based analysis and dashboard construction, Cloud Datalab for code-based data science, and connections to BI tools such as Tableau, Looker, Chartio, Domo, and more.

There are also a number of emerging data management tools from relatively small vendors that are worth mentioning: 

10. Looker BI

Looker BI: Business intelligence software and Big Data analytics platform that helps users explore, analyze, and share real-time business analytics easily. It captures and analyzes data from multiple sources to help make data-driven decisions.

Big Data Management And Analytics

Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

With big data analytics, you can ultimately fuel better and faster decision-making, modeling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open-source software such as Apache Hadoop, Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.

Big Data Management System

Big data systems gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1.  Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2.  Manage
Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3.  Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Big Data Management Jobs

If you love data processing, analysis, and computer programming and want to join one of the hottest fields, big data is the way to go.Top companies like Microsoft, Amazon AWS, LinkedIn, IBM, and more are all looking to expand their search into this field. At the time of this article, Indeed.com listed over 1600 full-time jobs in Big Data with estimated salaries ranging from $90K to $140K per year.

Top positions include Big Data Developer, Big Data Engineer and Big Data Architect where employees are responsible for building scalable, real-time big data analytics systems. The Internet of Things (IoT) is producing massive quantities of data and companies must find ways to gain more insights to stay competitive.

The demand for experts capable of architecting big data solutions is high and the salaries are extremely competitive. Programming languages and tools that you will need include spark, Hadoop, Sqoop, HDFS, MapReduce, Scala, Apache Spark, Apache Hadoop, Java, C++, SQL, Python and more to excel at your position.

Benefits of Big Data Management

Organizations that discuss using big data usually have the resources to hire research forms and data scientists to do the work for them. But, if you know where to look, small businesses can finally step up to the plate and utilize big data, themselves.

1. Using big data cuts your costs

A recent Tech Cocktail article looks at how Twiddy & Company Realtors cut their costs by 15%. The company compared maintenance charges for contractors against the average of its other vendors. Through this process, the company identified and eliminated invoice-processing errors and automated service schedules.

2. Using big data increases your efficiency

Using digital technology tools boosts your business’s efficiency. From using tools such as Google Maps, Google Earth, and social media, you can do many tasks right at your desk without having travel expenses. These tools save a great amount of time, too.

3. Using big data improves your pricing

Use a business intelligence tool to evaluate your finances, which can give you a clearer picture of where your business stands.

4. You can compete with big businesses

Using the same tools that big businesses do allows you to be on the same playing field. Your business becomes more sophisticated by taking advantage of tools that are available for your use.

5.  Allows you to focus on local preferences

Small businesses should focus on the local environment they cater to. Big Data allows you to zoom in on your local client’s likes/dislikes and preferences even more. When your business gets to know your customer’s preferences combined with a personal touch, you’ll have an advantage over your competition.

6. Using big data helps you increase sales and loyalty

The digital footprints that we leave behind reveal a great deal of insight into our shopping preferences, beliefs, etc. This data allows businesses to tailor their products and services to exactly what the customer wants. A digital footprint is left behind when your customers are browsing online and posting to social media channels.

7. Using big data ensures you hire the right employees

Recruiting companies can scan candidate’s resumes and LinkedIn profiles for keywords that would match the job description. The hiring process is no longer based on what the candidate looks like on paper and how they are perceived in person.

Oracle Big Data Manager

Oracle Big Data Manager is a browser-based tool that gives you broad capabilities to manage data across your enterprise. Oracle Big Data Manager is installed and configured by default when you create an Oracle Big Data Cloud Service cluster.

You can use Oracle Big Data Manager to connect to and interconnect a range of supported Oracle and non-Oracle data storage providers. After you register storage providers with Oracle Big Data Manager, you can preview data and (depending upon the accessibility of each storage provider) compare, copy, and move data between them.

With a Hadoop storage provider, you can also move data internally within HDFS, do data import/export and analytics with Apache Zeppelin, and import data into Apache Hive tables. You can also upload data from your local computer to HDFS.

Oracle Big Data Manager provides several methods for data transfer. You can use the console, which includes drag and drop data selection. Python and Java SDKs are available for building data management scripts and applications. There’s also a CLI for performing copy operations and for creating and administering data management jobs. 

The Oracle Big Data Manager administrator can create other user accounts and selectively grant access privileges to storage providers.

By default, Oracle Big Data Manager is started on port 8888 on the third node of a cluster, the same node as Cloudera Manager.

Data Management Techniques

Data management is a critical business driver used to ensure data is acquired, validated, stored, and protected in a standardized way. It is essential to develop and deploy the right processes so end users are confident their data is reliable, accessible, and up to date.

To make sure that your data is managed most effectively and efficiently, here are seven best techniques for your business to consider.

1. Build strong file naming and cataloging conventions

If you are going to utilize data, you have to be able to find it. You can’t measure it if you can’t manage it. Create a reporting or file system that is user- and future-friendly—descriptive, standardized file names that will be easy to find and file formats that allow users to search and discover data sets with long-term access in mind.

• To list dates, a standard format is YYYY-MM-DD or YYYYMMDD.
• To list times, it is best to use either a Unix timestamp or a standardized 24-hour notation, such as HH:MM:SS. If your company is national or even global, users can take note of where the information they are looking for is from and find it by time zone.

2. Carefully consider metadata for data sets

Essentially, metadata is descriptive information about the data you are using. It should contain information about the data’s content, structure, and permissions so it is discoverable for future use. If you don’t have this specific information that is searchable and allows for discoverability, you cannot depend on being able to use your data years down the line.

Catalog items such as:

• Data author
• What data this set contains
• Descriptions of fields
• When/Where the data was created
• Why this data was created and how

This information will then help you create and understand a data lineage as the data flows to tracking it from its origin to its destination. This is also helpful when mapping relevant data and documenting data relationships. Metadata that informs a secure data lineage is the first step to building a robust data governance process.

3. Data Storage

If you ever intend to be able to access the data you are creating, storage plans are an essential piece of your process. Find a plan that works for your business for all data backups and preservation methods. A solution that works for a huge enterprise might not be appropriate for a small project’s needs, so think critically about your requirements.

A variety of storage locations to consider:

• Desktops/laptops
• Networked drives
• External hard drives
• Optical storage
• Cloud storage
• Flash drives (while a simple method, remember that they do degrade over time and are easily lost or broken)

The 3-2-1 methodology

A simple, commonly used storage system is the 3-2-1 methodology. This methodology suggests the following strategic recommendations: 3: Store three copies of your data, 2: using two types of storage methods, 1: with one of them stored offsite. This method allows smart access and makes sure there is always a copy available in case one type or location is lost or destroyed, without being overly redundant or overly complicated.

4. Documentation

Within data management best practices, we can’t overlook documentation. It’s often smart to produce multiple levels of documentation that will provide full context to why the data exists and how it can be utilized.

Documentation levels:

• Project-level
• File-level
• Software used (include the version of the software so if future users are using a different version, they can work through the differences and software issues that might occur)
• Context (it is essential to give any context to the project, why it was created, if hypotheses were trying to be proved or disproved, etc.)

5. Commitment to data culture

A commitment to data culture includes making sure that your department or company’s leadership prioritizes data experimentation and analytics. This matters when leadership and strategy are needed and if budget or time is required to make sure that the proper training is conducted and received.

Read Also: How to Become a Digital Marketing Analyst

Additionally, having executive sponsorship as well as lateral buy-in will enable stronger data collaboration across teams in your organization.

6. Data quality trust in security and privacy

Building a culture committed to data quality means a commitment to making a secure environment with strong privacy standards. Security matters when you are working to provide secure data for internal communications and strategy or working to build a relationship of trust with a client that you are protecting the privacy of their data and information.

Your management processes must be in place to prove that your networks are secure and that your employees understand the critical nature of data privacy. In today’s digital market, data security has been identified as one of the most significant decision-making factors when companies and consumers are making their buying decisions. One data privacy breach is one too many. Plan accordingly.

7. Invest in quality data-management software

When considering these best practices together, it is recommended, if not required, that you invest in quality data-management software. Putting all the data you are creating into a manageable working business tool will help you find the information you need. Then you can create the right data sets and data-extract scheduling that works for your business needs.

Data management software will work with both internal and external data assets and help configure your best governance plan. Tableau offers a Data Management Add-On that can help you create a robust analytics environment leveraging these best practices.

Using reliable software that helps you build, catalog, and govern your data will build trust in the quality of your data and can lead to the adoption of self-service analytics. Use these tools and best practices to bring your data management to the next level and build your analytics culture on managed, trusted, and secure data.