4 pitfalls in your data strategy (and how to avoid them)

By Talend Team

If you’re a data leader at an early-stage or high-growth company, you’re in a unique position to promote data appreciation. Don’t waste your golden window of opportunity. Take this moment to institute best practices, promote good habits, and build the foundation for a data-driven culture.  

There’s no universal data strategy that would work for all organizations — wouldn’t that be great? — but there are pitfalls that all organizations should watch out for. Remember, now is always the critical moment to get your data strategy right. If you don’t, repairing the damage will take a lot more time and money down the line.  

Companies of all kinds have struggled with data 

You know that data matters. Not just to you and your team, but to the long-term success of your company. You rely on data to evaluate programs, monitor your place in the market, prioritize new initiatives, and more. So why does it sometimes feel like data is a battle — and you’re losing?  

Even in the most sophisticated or well-intentioned companies, data initiatives can falter or fail to deliver on their promises. A team pursues one path thinking the data is telling them to go that way when, in reality, the world is going another.  

If that sounds uncomfortably familiar, you may have also taken a wrong turn with your own data at some point. Missteps can be frustrating, but if you can recognize the problem and course-correct early on, you can save yourself a lot of stress in the future.  

The first step is to take an honest look at your approach and see where things could be going wrong.  

Four wrong moves companies make in their data strategy  

Pitfall #1: Thinking about data last  

Too often, data leaders and their teams get forced into a reactive role. A new strategy develops or a new opportunity arises, and the data team is the last to hear about it. They are forced to think on their feet, pulling reports, plans, tactics — anything that might be useful — out of thin air. Data teams find themselves acting as a service center rather than helping to define strategy from the outset.  

Making data an afterthought is an easy trap, especially for businesses that are moving fast. It practically guarantees that the data teams get caught on the back foot with nowhere to turn — until someone breaks the cycle. 

What to do:

It’s up to you to be a data champion, raising the question of data early and often. And when you talk to leaders across the organization, connect the dots between the data and business outcomes: 

  • Target: CRO. Tactic: point out the impact of healthy CRM data on shortening sales cycles and improving cross-sell and upsell. 
  • Target: COO. Tactic: communicate the impact that inefficient data processes are having across the company, and suggest practical solutions for saving time and money. 
  • Target: CMO. Tactic: show that you understand the vital function of customer data in marketing programs, and emphasize that collecting quality customer data requires sound data architecture. 

Pitfall #2: Overestimating your data readiness  

Everyone says they want to be completely data driven — but how realistic is that goal? As much as we would all like to be a shining example of putting data first in every single decision, not every business has the time, resources, or drive to make that happen.  

Research by McKinsey Analytics reveals that 61% of companies that acknowledge the impact of data and analytics on core business practices are still taking an ad-hoc approach to analytics instead of developing a full-fledged data strategy.  

What to do:

Make sure that every go-to-market–related meeting starts with a metrics review and every program gets assigned leading and lagging indicators for measuring success.  

To take it one step further, consider displaying live feeds of certain key reports in your company's messaging center. You could also push regular monthly or quarterly updates from each line of business to all employees, so everyone has a chance to keep on top of the latest developments. If you add a layer of meta-analysis to the data, they'll enjoy the certainty that the reported numbers are correct and up to date.  

Pitfall #3: Failing to think from first principles  

When it comes to data, it is very, very easy to lose track of what really matters. Companies frequently fall down a rabbit hole of irrelevant metrics, spinning cycles on complex dashboards that chart dramatic changes over time without ever answering a single question about the core business. How often have you found yourself staring at yet another report detailing monthly engagements on social media or the number of support tickets logged in a week, and asked, “Sure, but… what does it mean?” 

This mindset fuels a tendency to invest in shiny new tools without building a solid foundation first. If you’ve ever found yourself working on the “Tableau implementation project” that should have been the “customer intelligence project,” you know how self-defeating that can be.  

What to do: 

Always start with a clear strategy.  

Take the time to formulate the questions you would most like to answer— not the metrics that are easiest to capture. If your primary goal is to make sure sales is getting more high-quality leads, website traffic alone isn’t going to tell you much about how people enter the sales funnel or where and why they fall away. Instead, make a list of all the things you wish you could know.  

Similarly, before you bring in a new tool, make sure that you know the problem you’re trying to solve.  

Take the time to build enthusiasm around the initiative; not just within your team, but with other teams who may have use for the new tool. Set up time to train new users and schedule regular refreshes to keep adoption and enthusiasm high.  

“Now we can adjust our digital media advertising in real time to attract players, customize bets based on player behavior, and help traders adjust live odds.”

— Christofer Daussion, Head of BI & Data Platform, Betlic Group

Pitfall #4: Letting your silos control you  

No company can completely avoid data silos.  Even the smallest, three-person, stealth-mode startup probably has two engineers running applications on different servers. As long as everything is working and everyone knows where to find what they need, silos don’t have to be a problem. However, when data silos start creating confusion, black holes, and bottlenecks, it’s time to take action.  

The easy strategy often seems like a good way to tackle low-hanging fruit, but that doesn’t always mean it’s the most effective solution. For example, let’s say your HR department wants to launch a new people management tool — but it has zero integration capabilities with your other data systems. If there are only five people using that tool today, shouldn’t that be fine? Well, not really. While buying the new tool may be easy, you could be creating a lot of work down the road when that data becomes critical for other parts of the organization.  

What to do: 

If you look for inefficiencies within your company, chances are you’ll run into data silos. Instead of hunting for silos, search for the problems. Once you know what you’re trying to solve, break it down into manageable steps — and if that includes changing technology to break down a silo, make sure that everyone understands that long-term benefits and try to get them excited about the change.  

Three questions to ask about your data strategy 

The key to becoming truly data driven is to stop thinking of data as something you just move, store, and check on as an afterthought. Instead, focus on a vision of data health: measurement and management of data for better discoverability, understandability, and value.

By making data the language of how you do business, you and your entire team will understand how to use it better, how to make sense of it, and how to get more value out of it.  

Ready to start fostering a culture of data health? As you build your data strategy, start asking yourself these questions: 

  1. Do project kickoff calls include data/IT and business stakeholders from the outset?  Make sure each project is built with a clear understanding of the business outcome and the tech capabilities to deliver on the project. You’ll avoid costly and time-consuming course corrections that become necessary when critical details come to light late in the game.  
  2. Has our leadership team had candid discussions about where data can be most valuable to the business? Frank conversations about your data aren’t always easy, but they’re necessary. Understanding your strengths will tell you where to double down to establish differentiation, dominate a market, or build a solid foundation for your business. Meanwhile, acknowledging your areas of weakness gives you an opportunity to bake in extra layers of protection. 
  3. Do we have a culture that asks for data first? When you start with the answer, the temptation to misinterpret the data to support your position can be overwhelming. By establishing a culture that looks at the data first — not just before you propose an answer, but while you’re still formulating the question — you’ll stand your best chance of rooting out bias and blind spots. It can be uncomfortable, but it will make your business stronger in the long run.  

This piece originally appeared in Entrepreneur magazine: https://www.entrepreneur.com/article/390075