Gary Drenik: In continuing the discussion on the Single View of the Customer, the difference between success and failure depends on alignment and involvement from business leaders. These initiatives too often go off track as the business objectives and data challenges evolve too quickly for organizations to manage. One way to reduce this gap is to get all stakeholders involved with business data modeling from the beginning, keep them engaged throughout the process and drive alignment through storytelling.

I recently had the opportunity to sit down with Megan Kvamme (CEO and Founder, FactGem), Rehgan Avon (Founder, Women in Analytics Conference) and Dave Cherry (Executive Advisor, Cherry Advisory) to discuss the strategies to get and keep leadership involved, how to manage the dynamic nature of the data and models to achieve success.

Dave, last time we spoke on this topic, you shared your view of the primary components of a single view of data, including demographic, transactional, behavioral and purchase intention data. Let’s start by understanding how and why they are so dynamic.

Dave Cherry: As customers, we are always changing. Every day, our relationships, preferences, needs, interactions, purchases, and more are always shifting. Staying up to date on this within your own household is a challenge, let alone asking a retailer to stay on top of it for thousands of customers. As a result, the data needs of the business are also in motion. Historical data sources are no longer sufficient to evaluate business decisions. This necessitates a shift in the type or data (e.g. more sensor/IOT data, forward looking customer sentiment, social) that can help shed light on the these changing customer mindsets.

At the same time, customer expectations are rising exponentially, being set by non-traditional competitors that are raising the bar on experience and service across all sectors. Customers don’t see any differentiation. That means that they expect the same high quality service level from every provider. While many try to tackle this data challenge first, the better place to start is in driving alignment with business leaders on their specific objectives.

Drenik: Megan, how does one go about developing aligned business objectives?

Megan Kvamme: The good news is that successful CIOs get it. When gaps appear between line-of-business and IT, top CIOs look at that as a key leading indicator. They do everything possible to avoid any “us versus them” culture. Obviously, organizations that operate to key performance indicators (KPI) flow these down to CIO organizations. However, from a data standpoint, there’s a fundamental disconnect throughout the entire arc of computing – from mainframe to mobility. That disconnect is the way we’ve gotten used to modelling business data does not connect or resonate with business leaders. At first, this sounds like we’re getting into weeds, just a technical detail. In reality, if the words and pictures we use to communicate the business impact of data is the source of business/IT disconnect, this technical detail ends up infecting every aspect of an organization. If organizations can visually depict in simple diagrams how data is connected, especially the key entities and relationships and how they interact, it goes a long way to aligning business objectives with data management and application investments for ‘Run the Business’ and ‘Observe/Report on the Business’ type activities.

Drenik: So why is storytelling so important? If the analytics and data produce a clear answer or guidance for a business decision, isn’t that enough to gain alignment with the business leader?

Rehgan Avon: Data scientists spend most of their time searching for the information that patches together a story of what has happened or what is currently happening so that the business can conclude what might happen next at a certain level of confidence. Without context around the outcomes of these exercises, it is hard to say what decision to act upon. These exercises also include assumptions made along the way, stories communicated to the business leader tend to expose these and allow them to work together more efficiently and effectively to come up with the best solution.

Drenik: That makes a lot of sense. So what challenges do most data scientists face when trying to tell a story with their data and analytics?

Cherry: The primary challenge that most data scientists face is in making a connection, building a relationship and empathsizing with their business partners. Many lack industry, functional and operational expertise and hence really don’t fully understand the business objectives. Data scientists need emotional intelligence and consultatitve skills to help translate their quantitative findings into meaningful business recommendations. Developing these storytelling and influencing skills essentialy transform them into “business scientists”.

Drenik: Rehgan and Megan, what advice would you give to a data scientist to help them improve their storytelling skills and ultimately help deliver more valuable insights to their business partners?

Avon: Continuous and iterative communication about their findings allow the data scientist to understand what is important to the business and challenge their assumptions. Take the time to understand the business objective how the decision or analytis fits within it. Understanding the relevance of the data and how it connects together in the real world will paint a more realistic picture for the data scientist that will resonate with business leaders who understand the potential for change and positive impact.

Kvamme: Know your business leader and their level of interest and understanding of complex analytical methods and develop your story accordingly. Some business leaders just want the answer while others want to know exactly how you arrived at the result.
Then make sure you tailor your story to the specific business partner and put the business decision in context. Ensure that you have a breadth of storytelling skills and look for opportunities to learn new tools, data sources or techniques that can help. As an example, FactGem provides a platform that can be used to enable a business-oriented “whiteboard” discussion or show a more technical view. Both data scientists and business leaders can use the tool and tailor it to their specific needs.

Drenik: Thanks Megan, Rehgan and Dave.