How to Choose the Right Data as a Service Provider

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To select the best Data as a Service (DaaS) provider, first identify your unique business objectives and data requirements, then assess potential providers based on their expertise, service scope, technological stack, data security, scalability, and track record and reputation. In addition, obtain data samples to check relevance and correctness, learn about their data sourcing and methods, and clarify pricing structures and Service Level Agreements (SLAs) to ensure they match your budget and expectations.

1. Define Your Data Needs and Goals

Business Objectives: Clearly outline what you aim to achieve with data services, such as improving customer experiences or streamlining operations.
Data Characteristics: Determine the type (structured, unstructured, text, images), volume, and complexity of the data you require.

2. Evaluate Provider Expertise and Services

Domain Expertise: Look for providers with deep knowledge in your industry to ensure they understand your unique challenges and can provide tailored solutions.
Service Scope: Verify that they offer the full analytics lifecycle, from data collection and cleaning to model development and deployment.
Technical Skills: Check their proficiency with relevant technologies, including big data, machine learning, artificial intelligence, and the tools and platforms they use.

3. Assess Data Quality and Security

Data Relevance: Request data samples to assess the quality, accuracy, and relevance to your specific business needs.
Data Sourcing and Processes: Inquire about their data collection, cleaning, and update processes to build confidence in data integrity.
Security & Compliance: Ensure the provider adheres to data security regulations and has robust data governance practices, security measures, and disaster recovery plans.

4. Consider Technical Aspects

Technology Stack: Confirm their technology stack is up-to-date and compatible with your existing systems for seamless integration.
Scalability and Flexibility: Choose a provider that can deliver solutions that scale to meet your changing business needs.

5. Review Reputation and Vendor Relationship

Track Record and Reputation: Research their reputation through case studies, client testimonials, and reviews to gauge their quality of service.
Trust and Transparency: Build a trusted relationship with a provider who is transparent about their data sources, processes, and security measures.

6. Understand Cost and Contractual Terms

Pricing Models: Understand their pricing structure and how costs are managed.
Service Level Agreements (SLAs): Review SLAs for uptime, reliability, and disaster recovery to ensure performance and availability meet your expectations.

The first step is to gain a clear understanding of your data management requirements. Consider the following:

Data Types: What types of data do you collect and utilize? This could include customer information, website analytics, social media data, financial records, and more.
Data Volume: How much data are you currently handling, and how much are you projected to generate in the future?
Data Security and Compliance: Are there specific industry regulations or compliance requirements governing your data?
Data Goals: What are your objectives for data management? Do you want to improve customer targeting, optimize marketing campaigns, streamline operations, or gain deeper insights into market trends?

By addressing these questions, you can create a comprehensive picture of your data management needs. This will help you identify the specific services and functionalities required from a DMSP.

Here are some key factors to consider:

Data Management Expertise: Look for a DMSP with a proven track record in data management solutions. Their team should possess expertise in data architecture, data security, data analytics, and relevant industry regulations.
Scalability and Flexibility: Your data needs will evolve over time. A reputable DMSP can offer scalable solutions that can adapt to your growing data volumes and evolving requirements. Additionally, ensure they offer flexible deployment options (cloud-based, on-premises, or hybrid) to suit your infrastructure.
Security and Compliance: Data security should be a top priority. Select a DMSP that prioritizes data security with robust encryption protocols, access controls, and disaster recovery capabilities. Furthermore, ensure they comply with all relevant data privacy regulations applicable to your industry.
Data Integration Capabilities: Choose a DMSP that offers seamless integration with your existing data sources and marketing automation platforms. This will streamline data collection, analysis, and utilization within your marketing ecosystem.

Top 6 DaaS Providers to Choose From

Most provide a hybrid of the two techniques, with technical people working behind the scenes to provide you with services in as transparent a manner possible. However, the level of assistance and consultation included in your package will differ.

Here’s a quick overview of some of the most popular DaaS providers available today.

1. Google Cloud Dataproc

Google’s managed Big Data service has enjoyed fast growth since it went into general release earlier this year. Clearly it has been able to leverage their presence and reputation for cloud innovation into a package which industry has found attractive. It runs Hadoop and Spark on Google’s Cloud Platform and integrates with the BigTable storage and BigQuery analytics frameworks.

2. Amazon Web Services.

AWS is the collective name for Amazon’s cloud-based business tools and services. Their managed Hadoop service is called Amazon Elastic MapReduce and it runs on Amazon’s S3 storage infrastructure. It is the market leader in providing business cloud computing services and customers benefit from their world class data security infrastructure.

3. Microsoft Azure HDInsight

Microsoft’s strong presence in the business software market made it a sure bet that it would play for a slice of the BDaaS pie. It has built on its Azure cloud framework by increasingly adding in functionality and compatibility with open source technology such as Spark and Storm. For many organisations, a big plus will be and User Interface (UI) features that are immediately familiar to millions even if they have never been near an analytics dashboard previously.

4. Salesforce Wave Analytics

Salesforce built up partnerships with companies including Google and Cloudera to bring Hadoop-based Big Data analytics to its cloud data services. Wave Analytics uses a UI which will be familiar to any users of its market leading CRM software to enable dynamic visualisations. It is also optimised for use on smart phones and watches, making it a strong contender if you want to put real time analytics in the hands of a mobile or shop floor workforce.

5. Qubole Data Service

Less of a household name than others mentioned previously, Qubole was founded by former Facebook data scientists who saw a need for a self-service Big Data platform for enterprise. It is designed to be operated from a UI which assumes no prior experience of using Hadoop. As Qubole is not a cloud storage provider its solution can be configured to run on Amazon, Google or Microsoft cloud infrastructure.

6. IBM BigInsights on Cloud

IBM’s data management systems already have high penetration, so again it was natural that they would be looking to move into business cloud computing. By integrating advanced analytics tools it has put together a suite of services aimed at lowering the entry barrier to Big Data analytics. IBM has also forged partnerships with social media companies such as Twitter, making it easier to gain insights, and developed its own cognitive, natural language processing engine, Watson, allowing data to be queried and analysed using natural human language.