3 tips for building winning data strategies

Doug Bordonaro, Chief Data Evangelist

One of the most frustrating aspects of IT leadership is that successful projects don’t make headlines or hashtags anywhere near as often as the failures do. It’s understandable why so many CIOs fear their next BI / analytics project.

With failure rates often hovering just above 50 percent, and adoption rates a mere 21 percent for leading vendors, the odds aren’t exactly stacked in a CIOs favour. Most have been burnt badly by past experiences.

I’ve been buying, selling, managing, and implementing BI and analytics solutions for more than 20 years and have been involved in hundreds of projects. As we gear up for the next Big Data Analytics conference in London, I wanted to share three factors that almost all modern data strategy programs share in common.

It’s been my experience that if you consider each of these factors when you build your data strategy, your chances of success in everything that follows improves significantly

1.  Treat data as a utility, not a luxury

Almost everyone treats data as a luxury. Think about other luxuries in your life—single malt whisky, jewellery, cars. We savour those things, but we also lock them away so that only certain people can access them.

Sounds like our data, doesn’t it? And we don’t just use security controls to restrict access, we also use skill and position. Do you want to create your own reports? Go to training first. Do you want access to that data set? Bad luck – you’re not in the right department.

What’s the most important resource on earth? I’d argue it’s water. Life can’t exist without it.

If you truly believe in building a data-driven organization, start thinking of data as a basic human right and treat it like a utility precious utility, not a luxury.

All the other decisions you make about data will be driven by your perspective about how it should be treated. Until everyone in your business takes data for granted, expects it to always be available, and wouldn’t make a decision without it, you won’t truly be data-driven.

2. Bring data to the people

Once we accept that data is a utility, not a luxury, next we have to think about how to give people access to data. Solutions should be framed around getting data to people where they are, not getting the people to the data.

Here’s an example: let’s say that your Sales Director needs data to determine who her most successful salespeople are. The old way of answering this business problem would have been to look for BI software that best aligned with existing IT architecture.

Once implemented, you’d meet with the Sales Director and ask her to define the metrics that drive her business. You’d then build dashboards and reports to meet those requirements, and train the end users to use the solution. Maybe you’d also assign an analyst to the Sales department to answer questions that arise.

Turn that workflow on its head: first meet with the SVP, and ask her to define the metrics that drive her business. Make those metrics available in the tools the salespeople already use. Why should they leave Salesforce Chatter or Slack to ask questions? The technology exists to do that today.

Find solutions that enable users and that get the data to them; don’t require them to learn new tools and processes. It’s the difference between getting your water from the tap (a utility), or directions to the community well and 20 litre jerry cans to carry it in (a resource).

3. Solve big problems in small chunks

Not only has almost every BI project I’ve been involved with has been IT-driven; we often fail to ask end users for their requirements until it’s time to produce reports and dashboards. By then, it’s too late. What we’re able to produce is limited by the platform choices we’ve already made. Don’t even get me started on the number of projects that fail because the BI department spent six months implementing the perfect solution while the business moved on and did something else.

My advice: take an iterative approach—delivering small, valuable components to business users. Start with the most valuable business problem you can solve. Or that ‘third rail’ that nobody has dared to touch. This will buy you the trust and confidence you need to move ahead with further iterations.

I hope these three secrets have given you a starting point to help you build your next data strategies. After Big Data Analytics 2018, I’ll be sharing three more.

In the meantime, please visit ThoughtSpot (www.thoughtspot.com) at the show in stand 13 to learn how our search and AI-driven analytics platform can help you execute your data strategy.