No one can dispute that data has significant value for organizations. We see it every day in how they use data to successfully deliver better customer experiences – whether that means more personalization or better products and services based on collected and analyzed customer behavior.
Examples abound: we've all read how Netflix has used viewership data to design and produce new series that are adjusted to viewer behavior and preferences. Waze is also an example of the power of big data and analytics. Companies built solely on data, such as The Climate Corporation, have been successfully acquired.
Making analytics work
As easy and seamless as the end result may seem, many companies are still struggling to make data and analytics work for them. The data journey is indeed promising, but companies are still struggling with the fundamentals of data management: reigning data in, getting business units aligned with data solutions and creating data products that are embraced and adopted. The last point is critical because it highlights the importance of leadership and culture in successfully adopting a data and analytics culture. Progress is complicated even further by the noise created in the marketplace by things like big data, machine learning, artificial intelligence and the Internet of Things.
The truth is, a lot of these buzzwords have been hijacked by vendors that portray technology solutions as silver bullets that will solve all problems. They imply that by acquiring these technologies alone, companies can solve all of their challenges and start implementing solutions right away. Similarly, companies believe it's enough to hire data scientists, equip them with technology and just hope for the best.
We are beyond the point of asking why companies should get into data and analytics. Using data is a core competency that is starting to make a difference between organizations that succeed and those that fail. It is imperative for companies to define and start executing their data and analytics strategies as soon as possible.
That said, companies need to focus on a few things that I call the fundamentals of the data and analytics journey.
- Keep a balance between people, processes, and technology when designing and implementing data and analytics solutions.
- Implement data solutions that are aligned with business needs.
- Implement solutions in an agile way, in small iterations that deliver business value quickly.
Let's take a look at each of these points in detail:
Balance between people, processes, and technology
In my experience, this is one of the most fundamental elements companies should consider when delivering data and analytics solutions. It's also one of the most overlooked.
"We are beyond the point of asking why companies should get into data and analytics."
In the data and analytics space, the lack of balance between these three elements manifests itself in many ways.
- Technology: Many companies start their data journey by making significant capital investments in technology, such as business intelligence tools, database appliances, Hadoop clusters or similar components. Little do these companies know that they probably already have plenty of technology they can utilize. Furthermore, the latest cloud computing offers easy access to technology that can be consumed on-demand and allows companies to start without large investments. These aimless investments in technology can be traced back to business leaders who request that companies jump into data and analytics and technology teams that react by impulsively acquiring the technology.
- People: The lack of balance can also manifest itself on the people side. When companies hire armies of data scientists in hopes they'll deliver business value, this often results in fantastic algorithms that are disconnected from the reality of the business needs. I call these 'orphan algorithms' – perfect solutions with no problem to solve. Nowadays, there are plenty of ways to start small in this regard, such as relying on consulting companies. But first, organizations should define what they want to accomplish in a prototype kind of approach.
- Process: When it comes to the process, the lack of balance manifests itself when solutions are implemented without regard to adoption methods. For example, companies often implement a sophisticated forecasting algorithm that reduces process time from weeks to a few hours. However, sometimes the only problem is that the business team consuming the results of the forecast isn't ready for the solution. It's easy to assume that by producing a better forecast in a more efficient way, the business team will be able to adapt to it and 'run with it' – but that doesn't give the process changes enough consideration.
Alignment with business needs
Similarly to the points above, team's leading data and analytics work need to have a laser focus on the business needs of the company and how data solutions can help address them. This requires data teams to be in close sync with the business teams, with focused conversations on understanding what the real business problems are. Many times, the relationship between business and data teams is transactional in nature, putting data teams in an 'order taking' kind of role. Data teams need to elevate themselves out of this position, focusing the relationship on delivering high-value business solutions.
Agile solution delivery
Recently, I was in a session at a data and analytics conference led by Jared Souter, CDO, First Republic Bank. One of the key points he shared was the need for data teams to understand that momentum forward is more important than perfect trajectory. Data teams need to ensure that value is delivered quickly, in an agile way that allows business teams to realize concrete results in the short term.
"Many times, the relationship between business and data teams is transactional in nature, putting data teams in an ‘order taking’ kind of role."
This can and has to be done without losing sight of the long-term vision for data and analytics: short-term gains with a long-term view. Don't wait until your solutions are perfect or let a significant amount of time pass before implementing them. By the time the solution is delivered, the business context may have changed, rendering the solution useless. Delivering small, quick solutions also has the added benefit of allowing business teams to face less change when adopting new solutions.
Making data a core competency
There is no doubt that there is plenty of value to be realized from leveraging data as a core competency. As business processes become more digital, the amount of data available has increased significantly. Organizations that focus on the right priorities, in the right way, will be able to realize the most desirable benefits.