You can also get creative about where you get your data from, specifically external and new sources of data.
“Social media generates terabytes of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to weather forecasts.”
-McKinsey & Company
Even though the exact tools you use will vary based on company size and needs, all companies should have a single source of truth and a data dictionary.
Carl Anderson, a product researcher at WeWork provides more context in a TechCrunch article -
“At WeWork, a global provider of co-working spaces, we provide our analytics users with a core table called the “activity stream,” a single narrow table that provides web page views, office reservations, tour bookings, payments, Zendesk tickets, key card swipes and more. The table is easy for users to work with, such as slicing and dicing different segments of our members or locations, even though the underlying data comes from many heterogeneous systems. Moreover, having this centralized, relatively holistic view of the business means that we also can build more automated tools on top of those data to look for patterns in large numbers of different segments.”
Larger companies must be able to identify, combine, and manage multiple sources of data.
He continues with, “There are often historical reasons why data are siloed. For example, large organizations are more likely to acquire data systems through company acquisitions, thereby resulting in additional independent systems. Thus, a single source of truth can represent a large and complex investment. However, in the interim, the central data team or office can still make a big difference by providing official guideposts: listing what’s available, where it is and where there are multiple sources, the best place to get it. Everyone needs to know: “if you need customer orders, use system X or database table Y” and nowhere else.”
Identify potential data infrastructure tools (Data Warehouse, ETL, and BI tools)
Consider hiring a consultant. While it’s great that you’ve found your analytics lead, that person likely isn’t going to have the expertise required to put together all of the components of your tech stack or the experience to solve all of the different analytics problems you’ll face across your business. Mistakes made at this critical stage have serious costs in both time and money as you grow, so it’s important to lay a solid foundation.
Setup your data infrastructure
Build a data dictionary
The remaining 3 steps will help build a high functioning data-driven company that produces ROI.
5. Using the right processes to measure, predict, and optimize
Okay, maybe this is where the real fun begins. Think about a company like Spotify. 40% of its employees are dedicated to engineering and software.Many factors have led to Spotify’s success, but a key element has been the company’s commitment to data and technology.
If you’re a regular user, you’ve definitely noticed their personalized playlists based on your music preferences. How does Spotify categorize and recommend that music? You can thank their data science teams for that.
Once you decide on what data to track and you start to collect this data, then you can start to perform analytics, create dashboards, and optimize results. This is also when you put your hypotheses to test. Startups should use at least a weekly cadence for A/B testing.
If you want to learn more about the underlying mindset of this new environment, you can read more here:
The Mindset Of A Growth Practitioner — A New Growth Formula
A growth practitioner is someone that practices the strategy of making small experimental improvements every day.medium.com
“You can’t manage what you don’t measure,”
-W. Edwards Deming and Peter Drucker.
Think about Netflix’s ability to recommend the perfect movie during date night on Thursdays. It’s actually Valentine’s Day this Thursday (at the time this was written) and Netflix is going to recommend a certain set of romantic movies to a female living in New York City who loves adventure versus another set of recommendations to her male counterpart living in Columbus, Ohio who loves comedy. An important reason why that’s possible is because of the company’s ability to not only collect and track data, but their ability to accurately measure, predict, and optimize outcomes.
When you’re building a model, don’t focus on the data, rather focus on what the business opportunity is and how you can use the model to improve results.
Modeling that is test driven and hypothesis-led will not only generate faster outcomes, but the relationship of data and its business impact will also be better understood by the broader team.
I also think it’s important to recognize any modeling has its inherent risks. Therefore, companies should look for the least complex model that would improve performance without unnecessarily wasting company resources.
Build dashboards for specific goals using a BI tool
Implement a solid SQL process
Be thoughtful about your team structure
Implement data testing
6. Package the data so it can be easily digested, analyzed and reacted to
Here is where you get everyone on board by sharing the map. The entire company should be at ease with a data-driven approach. Make data easily accessible and don’t fall into the trap of using it to further a personal hunch. Don’t just use this new capability for one-off reports either.
This doesn’t mean you need to hand the keys out but you should think about the specific needs of your organization.
For instance, at Warby Parker, a retailer of prescription glasses and sunglasses, associates on the retail shop floor have access to a dashboard that provides details on their performance, as well as that of the store as a whole.
It’s also important to realize not everyone on your team will be comfortable with this approach and skills will vary. That’s why I believe coaching and training are vital to transforming culture (this also leads to the last step in this article). Dedicating office hours, email communication, and distributing a data dictionary are simple ways to support your team.
Make decisions based on data and challenge others to produce similar logic
Have time set weekly to discuss key metrics and their significance
Share results of weekly A/B test and model performance
Distribute data dictionary
Reinforce data literacy across the company
7. Transform your company’s capabilities into a competitive advantage
Maybe most critical for long-term sustainability, founders and senior leaders must possess the strength to transform the organization so that the data and models actually yield better decisions.
Make analytics part of the fabric of daily operations. This will inspire team members to view it as central to solving problems and identifying opportunities.
And remember, adjusting culture and mind-sets typically requires a multifaceted approach that includes training, role modeling by leaders, and incentives and metrics to reinforce behavior.
Build company dashboards using a BI tool
Use data when communicating during meetings and through email
Ask teammates for numbers to back up recommendations
Measure individual progress using key performance indicators
Get prescriptive (not just predictive)
Better late than never
Simply put — if you don’t have a data-driven culture, you’re missing opportunities to make incremental improvements in your business.
My years of experience have taught me that the collection and cleaning of data are usually the most difficult and time-consuming. The most rewarding part, generating insights for your team, might only take 20% of your time. Using this experience and the expertise of my team, at Northpeak we work from strategy and data warehouse setup to predictive analytics and machine learning. Having the opportunity to work with a diverse range of clients has shown me how incredibly difficult it is for companies to do well in this space.
Also, I want to acknowledge that some of this advice may vary and/or not pertain to you, as this requires a customized approach for each business. Some teams may be ahead of others in building a data-driven culture, but understand that this is a continuous process that is rapidly growing based on the evolution of technology — so it’s always good to revisit prior steps! I’ve seen if you can take this approach, your team can turn into a high functioning data-driven company and build serious long-term competitive advantages.
Lastly, but not least, remember that — data provides a picture into your company, but it does not drive your company, people do.
Get buy-in from company leaders
Create a data strategy
Choose the right data
Use the right tools to collect that data
Build processes to measure, predict, and optimize
Package the data so it can be easily digested, analyzed and reacted to
Transform your company’s capabilities into a competitive advantage
If you enjoyed this article or thought it was helpful, give it a few claps and share it with other community members.