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Everyone is talking about deploying analytics within their organization or department to obtain a competitive advantage through the power of big data. The truth is that incorporating an analytics reporting process into daily corporate operations is a demanding task. Here are some analytic success indicators to help you continue to improve your company’s analytic practices.

Get support from the corporate culture

The corporate culture matters more than anything because it either embraces or kills any big data analytics initiative.  Most departments that are not used to using analytic results to drive their business decisions are against anything that has to do with change, let alone any initiative using big data analytics.

To modify the corporate culture big data analytics efforts need support. The best way is to get it is from the top of the corporate structure.  Negotiate with high level sponsors to understand their needs.  Work to embrace the existing reports first to become informed on existing analytics, and then extend those with big data analytical reports through more formulas, data checkpoints and more data.  This helps everyone understand the starting context of the data available, the people supporting the infrastructure, and then more data points from the big data analytics.

Start at the top, work the bottom, and meet in the middle.

Most departments that are not used to using big data analytics to drive their business decisions are sometimes against anything that has to do with analytics initiatives.  As I said in the previous point, getting high level sponsorship is vital.  The key to understanding the bottom is analyzing the existing reports being used. 

Read Also: How to Build a Data Analytics Program

By understanding all the generated reports and then analyzing which ones are actually being used, you will understand which business areas need to be expanded with more data and more analytical points of comparison.  Use this report analysis to extend your big data analytics efforts to the middle of business. Report on areas that the top management and the rank and file workers can both use so they can meet in the middle of the company issues and find the analytics immediately beneficial.

Confirm and handle the truth

Big data analytical reports are not always pretty in the sense that they sometimes expose company issues, bad processes, or bad departments.  By looking at the existing reports and then extending them, your new analytics can confirm existing situations. Then the reports can be expanded for everyone who wants more analytical information on additional areas. 

One of the biggest challenges is when the new big data analytical reports points out bad news.  Handling the truth about a bad situation is never easy. Analytics reports that point this out will definitely bring an assault on the integrity, processes and big data used to develop the reporting.  Be ready, because sometimes even if your analytics are flawless and even though the message recipients shouldn’t, they do kill the messenger.

Start by thinking of the answer or outcome

Whether your analytics practices are just starting out or are mature, it is always best to think of the answers or outcomes first.  By thinking of the big data analytics output first (the amount of data, its type, comparison points, analytic formulas, etc.), assumptions and benefits can be discussed before the analytics begin.  Sometimes completing an analytical report or answer takes many intermediate steps, involves many data sources, and many important detailed integrity checks. 

Discussions of the overall process, its context, the data cleansing, different data sources, and final output formats are best refined through discussions with subject matter experts and business area experts that know the existing business processes.  Discussing the big data analytics processing and getting these subject matter experts and business experts involved is critical for the acceptance of the big data analytics results.

Build an analytics narrative.

Building a narrative or story around big data analytics is always the best way to describe the findings and provide a deeper understanding of the results.  The story behind the data can reflect the different aspects of how the results were determined such as the intense data cleansing, the acquisition of the diverse data sources, their relationships,  their value, and importance to the analytics.

This big data analytics narrative can be dramatically enhanced through good graphic displays of the results.  Through line, pie, bar graphs, location maps, and other graphic depictions of the analytic results, management and users of the data can quickly understand analytical processes, the analytics value, the data differential, and the deeper meaning of the analysis.

Clear, Strategically Aligned, Business Vision & Objectives

Structure often follows strategy which makes it imperative that the vision and strategy of a business is reflected in the solution. It is even more important to ensure that the solution build is flexible and supports the growing need for continuous change (CI/CD) and the DevOps approach. The vision and objectives should be short and to the point. They should also be articulated in a way where they can be quantified and measured. If you don’t know where you are going and why, might as well not go anywhere as it will not matter anyway.

With such rapid change and advancements in technology, it becomes very difficult to strategize too far ahead as there is a need for more agility. The strategy review cycles are important but need to become shorter with the awareness of continuous digital transformation in mind. A great example of this is Amazon’s six page memos which require executives to rethink strategy and its execution annually. One interesting question, in particular, is: how are you planning to use machine learning? This is a direct commitment to the use of data and information within the organization which is driven by Amazon’s second on command and is aligned to the company vision and strategy.

Appropriate Technology and Infrastructure

We still agree with many experts that this factor is important, but with the onset of Cloud and the flexibility it brings it becomes less important. Granted that the technology needs to be appropriate for the organization and have the ability to integrate well and be supported by organizational resources. This factor is becoming less important with time as technology is becoming more commoditized using a pay-as-you-use and scale up and down at the click of a button. The barriers to access to infrastructure and technology required to deliver a BI, Analytics or AI solution are almost zero.

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