Canada: Blog: Big Data – How Should It Be Approached?

In this article, Edward Olson, Practice Leader, Advisory Services at Crowe MacKay LLP, answers key questions on the topic of "big data".

I've been asked numerous times about the value of data analytics in a world where the volume of data is increasing at exponential rates. I won't list all the questions I've heard, but here are a few:

  1. What data do we have that is of value?
  2. What can/should be done with data?
  3. How can it be used to strategically leverage knowledge of customers, processes or products to bring greater value to my organization?
  4. If we're not taking advantage of "big data", are we falling behind competition?

In my profession I do a lot of consulting on risk management. Applying the discipline of assessing risk to data analytics, I see a significant risk being that people and organizations see "big data" as a tremendous opportunity and jump head long into the data without charting a course for where they want to go with data, how they want to use data, and what data is even relevant to them. To me, "big data" is like quicksand. Jump in while not being aware of what it is and you'll sink without turning it into a valuable tool. It can become a nightmare of accumulated data that is ever growing, even potentially called a "business intelligence tool", yet it is unable to provide the intelligence needed to make practical decisions in a timely many.

I see a need for a discussion to frame the process to using "big data" first, prior to starting actual data analytics. This post is meant to put some parameters around the discussion of data analytics and not to expand on how to carry out analytics themselves. Having a knowledge of the playing field will help you strategize on which plays to run – which data analytics are relevant and useful. It makes no sense, for instance, to start running a football down a cricket pitch – they're different games with different rules and different scoring. Data analytics is a useful tool leading to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals. For auditors, it leads to more comprehensive understanding of underlying transactions for testing and better ability to identify fraud. But know where to start before charging headlong into analytic activity.

So what should be considered first? What discipline should be applied to bring a suitable construct to the process of leveraging "big data" through data analytics? Corporations need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. What many organizations struggle with is that they do not have the right systems to capture the data they need or weren't collecting useful data in the first place. Lacking the right technology to store and access data is a necessary risk to evaluate. Look at the quality of data – garbage in will result in garbage out for analytic purposes. Collecting data is one thing, but being able to leverage and use that data is something completely separate. It may be that you have to change the process for data feedstock in order to ensure completeness and accuracy of the initial data set prior to any analytic actually being undertaken.

Next, a good data strategy identifies all relevant data sources and builds a complete data view of business processes in order to differentiate existing analytics capabilities and business needs. Then turn data into a story that makes it useful rather than just exposing it or displaying it. A narrative that gives you context to today's reality by exploring the trends and comparisons that you need in order to make sense of it all is necessary. You should identify important data relationships, information that will be useful for ongoing activities, and tools for monitoring the business that will give insight.

Follow these steps to bring a consistent approach to leveraging data analytics:

  1. Understanding of existing business strategy – what information/knowledge is needed to aide in achieving the existing business strategy?
  2. Set a data strategy – what is desired, when is it desired, and what outcomes are desired from leveraging "big data" through data analytics?
  3. Focus on identifying sources of data – what are you currently collecting and where are you getting it from? Map your data flow to have a macro level view.
  4. Identify the technology/systems accumulating this data – where are you collecting the data and through which technology platforms? Are your applications sufficient or should new interfaces be considered?
  5. Ensure the data is "useable" for analytic purposes – if you need quantitative data only, and yet the field collecting the data is a mix of quantitative and qualitative, ensure the process for collecting the data is cleaned up firstly so that the data is "useable" for your purposes. Also, is the data set complete or should additional information be collected on business relationships (i.e. customers)?
  6. Earmark desired sources of data that are relevant to your business – what data is of highest importance to you that will help you make appropriate business decisions? This should link directly to delivering on strategy.
  7. Create the story that will make the data useful – understand what relationships between data points gives business insight; identify which trending better assists with forecasting; establish the point where decisions need to be made or changes required to strategic direction.
  8. Utilize the tools necessary to manipulate and evaluate the data for desired outcomes and business intelligence – now that you have useable business data, evaluate the plethora of tools that can be leveraged to evaluate the entire population of "big data" to maximize its value. This is where you can apply your strategy to data analytics.

Just remember, keep it simple and build as you go. Jumping into the deep end may frustrate the process resulting in less engagement by executives and boards on what is truly a tremendous opportunity for all corporations to embrace. Follow some of the basic points identified above for due process to understand the "why" which will then lead into the "what makes sense" and ultimately will deliver business intelligence that will enhance daily business decisions. Have a disciplined approach and keep it simple.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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