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Big Data 4th April 2018 - 4 min read

5 Ways to Turn Big Data into Big Business

By Joni Lindes

Big data and machine learning have generated a lot of hype. Data analytics is used to transform the way businesses make decisions. The number one critique? Failure to deliver business value. PredictX believes that business value is easily achieved as long as data driven analysis and insight communicates with business goals effectively.

Rosemary Barnett at Techcrunch critiques that utilising big data will not deliver the value that is promised:

“Clicking through pages of  ‘unlock the value of your big data!’ advertorials, a cynic might suspect that the best (and perhaps only) method of deriving value from big data is to go into the business of telling people how to get value from their big data.”

The incorporation of data driven insight into business practices has potential to transform your business- cutting costs and creating greater profit. Greater insight into customer choices and stock management can cut costs and increase profits in the private sector. Using predictive data to develop actionable insights can reduce public spending while bettering the community on the public front. If this is the case, why is there criticism about data driven insight translating into actual business value? The short answer is making use of data driven insight in your organisation can introduce disruption to traditional company silos, barriers and ways of working.

When the entire business is not situated around making better decisions sooner one can analyse all the data you want but the predictive analysis will not necessarily translate into action. No action? No return of investment either.

Below are 5 tips to create a “culture of data” in your corporation and transform data analysis into profit.

1. Decisions before Data and Vice Versa

Big data and predictive analytics will only deliver streamlined data in a pretty dashboard if you don’t decide what questions you want that analysis to answer. Tapping into insight found in your data is vital yet is often missing some context.

On the other hand, most businesses do not want to use data analysis to make decisions. They already have decisions in mind and want to use values derived from data to back these decisions. While knowing what you want is a powerful tool it can also deliver metaphorical blinders leaving you closed to any conflicting insights.

The best method is to simply ask: What decisions do you want to make? How will data driven insight help make these decisions?

For example, don’t ask “Should I manage my staff in this manner or that manner?” Rather ask: “What is the most profitable way of managing my staff? This will open the business to added insight while keeping a goal in mind when sifting through the data. In order to deliver insight from data, business questions and decisions need to inform the analysis process. Likewise, data driven insight can in turn inform profitable company decisions.

2. Engagement Leads to Understanding

Often data driven insights are written for analysts who have expert understanding of mathematical and scientific elements. This fails to address decision maker’s needs so they can derive value from the numbers.

This leads to dashboard blindness where numbers change yet executives fail to derive meaning from these changes. Presenting results in an understandable and engaging way is key to getting the information to senior management so they can make decisions required to push business models forward. PredictX uses The Story to present their findings to stakeholders. The aim is to share understanding and transformation from the analysts’ desk through to key decision makers in the company. Only then is transformation achievable.

3. Break Down Traditional Barriers to Make Room for Collaboration

The largest obstacle for data driven insight to deliver success is traditional organisational boundaries preventing technical and business collaboration. When analytics is owned by only one department, the ability it has to transform the whole business becomes limited. Different departments have different goals, mindsets, levels of understanding and ways of working. Designing data analytics and generating insight from it must work across these different worlds. The technical levers in an organisation must learn how to effectively collaborate with business executives so business goals can frame the data collected and data can in turn inform and achieve business goals. IT departments need to be in the know so insights can be effectively put into action on a company-wide level. Integration will ultimately lead to success.

4. Don’t Ignore the Scientists

Finding correlations is the cornerstone of data analytics. We want to bring trends to the surface so actions can be taken, however oversimplifying these correlations can ignore the complexity that lies beneath what is seen as an “obvious” trend. Whenever scientists perform experiments they develop a hypothesis, perform a test, collect the data and perform it again while examining all potential causes underlying each observation. Big data, in its sheer complexity, generates even greater opportunities for error in analysis. When it comes to forming predictive analysis, opportunities for error increases. As much as we want to act on trends, these trends need to be consolidated, tested and verified. Specialists are indeed needed to examine the results.

5. DON’T Go Big, But Don’t Go Home Either

Senior executives have big goals in mind regarding how data-driven decisions can transform their business. Data can be used to achieve these goals but it is a journey, not a quick fix. If an analytics team only focuses on huge big picture wins early in the game, executives can lose interest when they fail to see results. Any business goal requires breaking it into smaller, achievable steps to create something which can transform the way we do business. Focus on smaller projects that deliver actionable results and executives will soon see the value data can deliver. It is also useful to discuss data analysis opportunity in business-friendly language focusing on the most pressing issues or industry-related dilemmas.

 

Joni Lindes
By Joni Lindes
4 min read

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