In today’s competitive marketplace, companies everywhere have seen the light of big data analytics. In almost every industry, businesses are streamlining operations, reaching out to more customers, and reducing expenses in new and exciting ways, made possible by data analytics. All of these key elements become part of a comprehensive data strategy; a strategy that is essential for companies looking to get ahead of the pack or to retain their position as an industry leader.
Although executives are increasingly moving towards data-driven strategies, many are committing huge errors in their approach. Some CxOs have inflated expectations, and quickly abandon data science when it proves less than the miracle cure they were seeking. Others rush in underprepared, often working with low quality data, with little-to-no idea of what sorts of results they are looking for. But a focused, comprehensive data strategy with clear goals can work wonders for a business. With this in mind, here is a step-by-step guide for companies looking to enter into the world of big data analytics, to try and avoid the pitfalls made my many, to achieve true benefits from data science.
- Start with a clear focus
Introducing data strategy into an organization can be difficult, but without a clear end goal in mind, it’s near impossible. Start by strategizing a main focus for your company. Ask questions like: what is the problem we are solving? What is the need we are trying to address? By figuring out the end goal early on, you’ll avoid wasting time on unnecessary endeavors, and develop an analytics platform specific to your organization. For example, in a manufacturing company, the issue may be in processing operations (such as machine downtime), or in marketing, it may be discovering customer behavior on social media sites. Each of these examples can be correlated to different data strategies, and should be treated as such.
- Use measurable metrics for success
Once you’ve determined a specific focus for the project, the next step is to set and track progress with measurable criteria along the way. Metrics could be anything from collecting quality data from X number of hospital patients, analyzing insurance claims based on X number of variables, or having a functional and accurate data visualization by X date. Having defined markers of your success is essential in moving forward with your data, building momentum and developing new goals once old ones are reached.
- Build a cross-functional business domain team
Your company’s data team should be filled with those who deeply understand the technology, such as IT architects, IT analysts, and analytical modelers, but just as important, with executive decision-makers, who profoundly understand the information being processed. The leader of the data team should be an operations manager, rather than an analyst. The expertise of these managers lies in regular problem solving, daily pragmatism and an integral understanding of the issues at hand.
- Qualify your data requirements
Evaluate your data needs to understand exactly what you are looking for: How is this data going to be collected? How accurate will it be? Can you rely on previously held data, or will you need to rethink how data is collected, stored and processed? Data quality is one of the biggest pitfalls of analytics, and can be a costly mistake in an analytics platform.
- Trust the data
When you have collected and processed the data, created a visualization, and revealed a clear pattern, the results can often be completely unexpected. As long as you have carefully and accurately built your data platform, with trusted sources and precise data, do not be afraid to trust the insights you gain. Many times, a company’s entire corporate strategy and direction shifts once appropriate data has been analyzed. In this day and age, making important business decisions with your gut is fine and admirable, but even better when your gut is supported by petabytes of data.
Lastly, after you’ve gone through the trouble of determining the problem, set performance metrics, built a capable data team, qualified the data and received results, the most important thing is to take action. The insights gained from solid data strategy are invaluable, and can be the deciding factor in how your company succeeds. Data strategy can determine whether you are a leader in your industry, or just another in the pack.