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Six Big Data Insights Help Your Customer Reach Their Goals

June 02, 2017

Six Big Data Insights Help Your Customer Reach Their Goals

Most executives envision of big data as a means to comprehend the inner workings of their particular clockwork universe, understanding how every aspect of their operation interacts with customer attitudes and other variables so they can accurately predict business outcomes. The ROI from big data is not in answering universal questions, but in creating smaller, actionable big data insights. Harnessing big data insights does not give you a crystal ball. However, if you know how ask the right questions and sift the available data to uncover actionable insights, then big data projects can yield real value.   

According to a report from the MIT Sloan Management Review, top performing companies have developed a habit of using big data to drive everyday decision-making. Of the companies surveyed, 45 percent of top performers used big data to guide future strategies as opposed to 20 percent of lower performers, and 53 percent of top performers used big data to guide everyday operations as opposed to 27 percent of lower performers. The top performing companies have learned how to mine the big data mountain to uncover real big data insights.

Small Data Yields Actionable Big Data Insights

If you think about it, big data is just a byproduct of the digital age. The vast surplus of available digital information is just industrial waste, the residue of social interaction and consumer transactions, much of which is meaningless without context. To deliver big data insights, you have to start small by defining questions that will put your data context. That requires a step-by-step approach to wrest big data insights from big data chaos.

Here are six big data strategies to help your customers get big data insights that can help them achieve their goals.

  1. Start with a goal in mind – If you take a mountain of data and sift it looking for information you will learn a lot of things and see patterns you never saw before, but is that insight valuable? It’s better to ask a question and have an idea of what specific types of information you want to uncover. If you start by looking for answers to specific questions, it will be easier to filter out the data points that are most valuable and see what might be missing.
  2.  Be sure to add context – Meaningful insights don’t come without context. Understand the business problem you are trying to solve and analyze related data to look for patterns and identify anomalies. You might be asking the right questions but if you apply data out of context the answers you get won’t have value – garbage in, garbage out.
  3. Apply proven analytic techniques – Hadoop isn’t the only approach to big data analytics. Analytic tools can be written in UNIX, Ruby, and other programming languages as well as Apache Pig and Hive for Hadoop. But whatever analytics tools you choose, be sure to use proven analytic technique such as linear regression to show correlations.
  4. Text can provide context – Text analytic tools can be useful for unveiling big data insights from unstructured textual data, such as social media. The proper text analysis can create context from conversations that will reveal hidden patterns.
  5. Use machine learning to make predictions – Machine learning algorithms are becoming more sophisticated all the time, and are much better suited to analyzing predictive data sets. For example, with the right tools and data sets, machine learning can be applied to modeling fraud detection or for retail pricing analysis. Just be sure you have a firm understanding of the machine learning tools you are using so you can rely upon the results.
  6. Visualize the results – Big data insights should tell a story. To make the findings clear, create a visual narrative the graphically highlights trends. The story the data tells should be easy to interpret and graphics make it easier to identify trends and determine next steps.  If you can create a set of graphic tools at the outset, it’s also easier to model the data again and again to reveal changes and patterns over time.

You don’t have to strip-mine the mountain of data to generate big data insights. By asking clearly defined questions and having a clear idea of the outcome you want to achieve, you can apply the right tools and methodologies to deliver findings that are both meaningful and actionable. If you keep the scope and approach to big data mining within reasonable parameters, you will get better insight to help customers achieve their business goals.

What’s your biggest obstacle in delivering big data insights for customers? Is there a specific step in the process that provides a bigger challenge?