Data & Analytics = Big Potential

Juan FGorricho, Chief Data and Analytics Officer for Partners Fede, The Walt Disney Company
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Juan FGorricho, Chief Data and Analytics Officer for Partners Fede, The Walt Disney Company, Juan F. Gorricho is currently the Chief Data & Analytics Officer for Partners Federal Credit Union. In this role, Juan leads the data and analytic...  More >>

No one can dispute that data has significant value for organizations. We see it every day, in many different shapes; from better products and services that companies create based on collected and analyzed customer behavior, to personalized customer experiences. Examples abound: we all have read how Disney is creating magical experiences by leveraging data collected through the magic bands. Netflix has used viewership data to design and produce new series that are adjusted to viewer behavior and preferences. Companies have been completely built (and successfully sold) on data such as The Climate Corporation.

As easy as it may seem, still many companies are struggling to make the whole data and analytics thing work. In a few recent conferences I have participated in, most of the companies I talked to are struggling with the fundamentals of data management: reigning data in, getting business units aligned with data solutions, and, more importantly, getting data solutions to be adopted and used. Making progress is more complicated by the noise created in the marketplace by things like big data, machine learning, and internet of things. A lot of these terms have been hijacked by vendors making it seem as silver bullet solutions. The perception has been created that by acquiring these technologies alone, companies can solve all their challenges and start implementing solutions right away: add water and that is it. Something similar is happening with people side of the equation: companies are hiring data scientists, equipping them with technology, and hoping for the best.

  ​Making progress is more complicated by the noise created in the marketplace by things like big data, machine learning, and internet of things  

In my experience, companies need to focus on a few things:

• Keep a balance between people, process, 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.

 Balance Between People, Process, and Technology

There is nothing that sounds more like a cliché in the technology world that “keep the balance between people, process, and technology.” However, in my experience this is one of the most fundamental elements companies need to take into account when delivering data and analytics solutions, and one that is often and easily overlooked. In the data and analytics space, the lack of balance between these three elements manifests itself in many ways. Companies that want to jump into the data and analytics bandwagon, purchase large amounts of technology in the shape of BI tools, database appliances, Hadoop clusters, or many other similar components.

The belief is that by purchasing and deploying that technology, useful solutions will come out of them. In this case, the technology dimension is completely out of balance. In my experience, companies already have plenty of technology with which they can get going. Furthermore, cloud computing nowadays offers easy access to technology that can be consumed on demand and that allows companies to start without large investments. In my experience, the driver of this aimless purchase of technology can be traced back to business leaders requesting that companies jump into data and analytics; technology teams react by acquiring technology or in many cases, business teams purchase technology themselves that then gets dumped into IT’s hand for management. The best way to avoid this scenario is for technology teams to elevate the conversation with their business counterparts, focusing on what they want to accomplish rather than on the business telling them what to do.

The lack of balance can also manifest itself on the people side. Companies have attempted to jump into the data and analytics party by hiring armies of data scientists. Organizations think just by hiring data scientists, valuable data, and analytics solutions, business value will be delivered. Reality is that in many cases, companies end up with groups of smart people that are creating fantastic data and analytics solutions but that are disconnected from the reality of business needs. Solutions for which, problems need to be analyzed. Nowadays, there are plenty of ways to start small in this regard. For example, there are plenty of companies offering data scientists in consulting engagement ways. Organizations should define what is it they want to accomplish and partner with these companies to start small, in a prototype kind of approach.

On the process side of the people, process, and technology equation, lack of balance manifests itself when solutions are implemented without taking into account the changes that business processes need to go through in order for the data solutions to be adopted. A simple example of my experience is one time in which we implemented a sophisticated forecasting algorithm that reduced process time from weeks to a few hours; the business team consuming the results of the forecast wasn’t ready for the solution. We assumed that simply by producing a better forecast in more efficient way (too much emphasis on technology), the business team would be able to adapt to it and “run with it” (ignored the process changes).

Alignment with Business Needs

Similarly to the points above, teams leading data and analytics work need to have a laser focus on the business needs of the company and on how the data solutions can help address those needs. This requires data teams to be in close sync with the business teams, focusing conversations on understanding what the real business needs 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. As simple as it sounds, it is a critical area that can make a big difference in the success of data teams.

Agile Solution Delivery

Recently, I sat in a session at a data and analytics conference led by Jason Souter, CDO, First Republic Bank. One of the key points he shared with us was the need for data teams to understand that “momentum forward is more important than perfect trajectory”. I really liked the simplicity with which he explained it. Perfectly in alignment with the points I have highlighted above, 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. This can and has to be done without losing sight of the long term vision of where the data and analytics team wants to be in the future. Short term gains with a long term view. I have been part of efforts and I have seen many projects that want to make sure their solutions are perfect. They take a significant amount of time to deliver the perfect solution. In this scenario, what usually happens is that the solution delivered may be the right solution at the wrong time (too late). By the time the solution is delivered, the business context has 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.

This is a time in which companies can become more competitive by using data and analytics. Technology is no longer a barrier and as business processes have become 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 large business benefits. All that is needed is a little bit of common sense and a strategic view of what the ultimate business goal should be.

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