Throughout last week’s event, our booth was jam-packed with people across industries seeking to learn more about how to succeed with their data programmes. It’s a broad topic and approaches vary depending on industry and company size. However, certain insights are broadly applicable, which I’ll share in this post.
I ended by recommending that problems be solved by delivering iterative value. But how do we find those valuable problems in the first place? My first two tips “Do Better “and “Do More” explore this in more depth. The third “Always ask for data” is my top tip for achieving the cultural change required to maximize data analytics potential in your business.
- Do better
This is the ‘low hanging fruit’. Identify a key pain point that’s shared across teams, something that everyone agrees should be done better. If one of these pain points is not immediately apparent, I guarantee you can find one out if you spend just a few minutes talking to your line of business colleagues and end users.
An easy place to start is your sales, finance and marketing directors. Ask them how their people spend their time, where they feel it’s being wasted, and what questions they have they cannot answer. What’s bottlenecking their decision making or productivity? Which tasks are completely pointless? Most importantly: which are causing customer problems?
Although opportunities to “do it better” are often glaringly obvious when it comes to analytics, there are a few I keep encountering.
Sales. Salespeople in service industries often meet with customers to get performance feedback. In those meetings customers invariably ask salespeople impromptu questions. How often are salespeople unable to answer these using their existing reports? What’s the turnaround time to answer those questions? If the answer is anything less than one minute, you’ve found a problem to solve.
Marketing. How successful is your current campaign? This question is often answered by a predefined report. Inevitably, someone in the management team will ask a question that the report doesn’t answer – another problem to solve.
Finance. Much financial analysis is static and repeating, but there is also considerable ad-hoc analysis. For example, finding expense report outliers. How do your financial people find out why software expenses are so much higher this quarter? It’s not unusual for these follow-on financial questions to take days or more—and that means it ripe for improvement.
- Do more
Doing something more with analytics is often about monetising data. However many CDOs I talk to about this get paralysed by privacy concerns. This is understandable, particularly in light of GDPR, but there are many ways to monetise data that don’t run afoul of privacy issues.
One is to resell aggregated data. Rolled up to a high enough level, data is completely anonymous. For example, if you are a satellite TV provider, resell viewing figures for programmes – in near-real time if possible.
Another is to resell aggregated data to another market. Many investors rely on the government’s regularly published Consumer Price Index figures, but these are limited in value. If you’re a retailer, you may have estimated figures for months in advance based on aggregated sales numbers. There’s a market for this information.
Customer self-service. Do you provide information back to individual customers about your product or service? Are they satisfied with the service? How many ad-hoc requests aren’t served by current reports? What’s the turnaround time and cost for those? Many customers are increasingly willing to pay a premium for having access to their data instead of having to ask for it.
- Always ask for data
The ultimate goal of any data strategy is to change an organisation’s culture so that everyone uses data to make better decisions. This culture change takes time and results from lots of small actions taken consistently. I often get asked how leaders can help contribute to this change, and the most important small action I’ve found you can take: Always ask for data.
When a colleague says they’re going to try something new, ask them what data they’ll use to measure the success. When a sales manager tells you they’ve set an ambitious target for the quarter, ask them how they know. Have they put it in historical context so that they know it’s really ambitious? When a decision has been made to choose a specific product, approach, or path, ask to see the data behind these decisions.
Ask often enough and soon enough people will form the habit of viewing and preparing data arguments before they come to you. That’s the end game.
Hopefully these latest tips, along with my first three have given you some practical ideas to help build your winning data strategy. For more inspiration, visit www.thoughtspot.com to find more advice on data strategies, customer stories and tools.