Guest blog post by Bernard Marr
One of my favorite examples of why so many big data projects fail comes from a book that was written decades before “big data” was even conceived. In Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, a race of creatures build a supercomputer to calculate the meaning of “life, the universe, and everything.” After hundreds of years of processing, the computer announces that the answer is “42.” When the beings protest, the computer calmly suggests that now they have the answer, they need to know what the actual question is — a task that requires a much bigger and more sophisticated computer. This is a wonderful parable for big data because it illustrates one quintessential fact: data on its own is meaningless. Remember the value of data is not the data itself – it’s what you do with the data. For data to be useful you first need to know what data you need, otherwise you just get tempted to know everything and that’s not a strategy, it’s an act of desperation that is doomed to end in failure. Why go to all the time and trouble collecting data that you won’t or can’t use to deliver business insights? You must focus on the things that matter the most otherwise you’ll drown in data. Data is a strategic asset but it’s only valuable if it’s used constructively and appropriately to deliver results.
Source for picture: click here
Good questions yield better answers
This is why it’s so important to start with the right questions. If you are clear about what you are trying to achieve then you can think about the questions to which you need answers. For example, if your strategy is to increase your customer base, questions that you will need answers to might include, ‘Who are currently our customers?’, ‘What are the demographics of our most valuable customers?’ and ‘What is the lifetime value of our customers?’. When you know the questions you need answered then it’s much easier to identify the data you need to access in order to answer those key questions. For example, I worked with a small fashion retail company that had no data other than their traditional sales data. They wanted to increase sales but had no smart data to draw on to help them achieve that goal. Together we worked out that the questions they needed answers to included:
- How many people actually pass our shops?
- How many stop to look in the window and for how long? How many of them then come into the shop, and
- How many then buy?
What we did was install a small, discreet device into the shop windows that tracked mobile phone signals as people walked past the shop. Everyone, at least everyone passing these particular stores with a mobile phone on them (which nowadays is almost everyone), would be picked up by the sensor in the device and counted, thereby answering the first question. The sensors would also measure how many people stopped to look at the window and for how long, how many people then walked into the store, and sales data would record who actually bought something. By combining the data from the sensors placed in the window with transaction data we were able to measure conversion ratio and test window displays and various offers to see which ones increased conversion rate. Not only did this fashion retailer massively increase sales by getting smart about the way they were combining small traditional data with untraditional Big Data but also they used the insights to make a significant saving by closing one of their stores. The sensors were able to finally tell them that the footfall reported by the market research company prior to opening in that location was wrong and the passing traffic was insufficient to justify keeping the store open.
Too much data obscures the truth
Really successful companies today are making decisions based on facts and data-driven insights. Whether you have access to tons of data or not, if you start with strategy and identify the questions you need answers to in order to deliver your outcomes then you will be on track to improve performance and harness the primary power of data. Every manager now has the opportunity to use data to support their decision-making with actual facts. But without the right questions, all those “facts” can conceal the truth. A lot of data can generate lots of answers to things that don’t really matter; instead companies should be focusing on the big unanswered questions in their business and tackling them with big data.
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science