By: Bernard Marr, Founder and CEO, Advanced Performance Institute and best-selling author & keynote speaker
I believe data should be at the heart of strategic decision making in businesses, whether they are huge multinationals or small family-run operations. Data can provide insights that help you answer your key business questions (such as ‘How can I improve customer satisfaction?’). Data leads to insights; business owners and managers can turn those insights into decisions and actions that improve the business. This is the power of data.
In this post I look at the process for applying data to your decision making – broken down into a simple ten-step process. Don’t be tempted to skip steps or jump ahead to juicier parts – the strategic steps are as important (if not more) than the data itself.
1. Start with strategy
It’s easy to get overwhelmed by the possibilities that a big data world provides, and it’s easy to get lost in the noise and hype surrounding data. Starting with strategy helps you ignore the hype and cut to what is going to make a difference for your business. Instead of starting with what data you could or should access, start by working out what your business is looking to achieve.
2. Hone in on the business area
You now need to identify which business areas are most important to achieving your overall strategy. If you could only work on improving one or two areas, which would you choose? For most businesses, the customer, finance and operations areas are key ones to look at.
3. Identify your unanswered business questions
Now that you’ve identified your strategic objectives, the next step is to work out which questions you need to answer in order to achieve those goals. By working out exactly what you need to know, you can focus on the data that you really need. Your data requirements, cost and stress levels are massively reduced when you move from ‘collect everything just in case’ to ‘collect and measure x and y to answer question z’.
4. Find the data to answer your questions
The next step is to identify what data you need to access or acquire in order to answer these questions. It’s really important to understand that no type of data is inherently better or more valuable than any other type. Focus on identifying the ideal data for you: the data that could help you answer your most pressing questions and deliver on your strategic objectives. Make a note of which data sets you could use to answer those questions. You can then choose the best data options to pursue based on how easy the data is to collect, how quick and how cost effective it is.
5. Identify what data you already have
Once you’ve identified the data you need, it makes sense to see if you’re already sitting on some of that information, even if it isn’t immediately obvious. Internal data accounts for everything your business currently has or could access. If the data doesn’t already exist, then find ways of collecting it either by putting data collection systems in place or by acquiring or accessing external data.
6. Work out if the costs and effort are justified
Once you know the costs, you can work out if the tangible benefits outweigh those costs. In this respect, you should treat data like any other key business investment. You need to make a clear case for the investment that outlines the long-term value of data to the business strategy. Although the cost of data is falling all the time, it can still add up if you get carried away. This is why it’s crucial to focus only on the data that you really need. If you believe the costs outweigh the benefits, then you may need to look at alternative data sources.
7. Collect the data
Much of this step comes down to setting up the processes and people who will gather and manage your data. You may be buying access to an analysis-ready data set, in which case there’s no need to collect data as such. But, in reality, many data projects require some amount of data collection.
8. Analyze the data
You need to analyze the data in order to extract meaningful and useful business insights. After all, there’s no point coming this far if you don’t then learn something new from the data. The most common types of analytics are text analytics, speech analytics and video/image analytics. The past few years have seen an explosion in the number of platforms available for big data analysis. Some platforms require nothing more than a working knowledge of Excel, meaning most employees can dip their toes into big data analysis. However, in many cases, data requires a more experienced analytical hand.
9. Present and distribute the insights
Unless the results are presented to the right people at the right time in a meaningful way then the size of the data sets or the sophistication of the analytics tools won’t really matter. You need to make sure the insights gained from your data are used to inform decision making and, ultimately, improve performance. These days there are more interesting ways to present data and exciting tools to help you do it.
10. Incorporate the learning into the business
Finally, you need to apply the insights from the data to your decision making, making the decisions that will transform your business for the better … and then acting on those decisions. For me, this is the most rewarding part of the data journey: turning data into action.