Four Lessons from the 2018 Gartner Summit
Last week, our Periscope team headed to Texas to attend the 2018 Gartner Data and Analytics Summit. It was a great opportunity to meet face-to-face with some of the best minds in data and discuss important industry trends. While there’s been a noticeable focus on collecting and analyzing data, there are still challenges that need to be addressed to get the most value out of data-based insights.
According to Gartner, the way forward involves establishing the role of Chief Data Officer, a hybrid technology and business executive who manages the entire data organization and optimizes the strategy for business outcomes. In fact, Gartner predicts that by 2019, 90% of large organizations will have hired a CDO. As these companies plan for a new executive to make better use of their data, Gartner offered four important considerations:
Data is only valuable if it is trusted
The concept of using data to make decisions is based on objectivity — numbers have fewer biases than humans and company-wide data can often take into account more points of view than a single, subjective person. As valuable as data-driven insights can be, they don’t translate into business value unless individual decision makers trust the information enough to translate it into effective decisions. While data teams have grown a lot in recent years, there’s still a trust gap that prevents their recommendations from evolving into accepted practices.
From a tactical standpoint, an easy way to bridge this gap is to improve the accuracy and consistency of data through lineage tracking, enabling collaboration with data stewards and building a hierarchy of truth that is accessible across the organization. These steps ensure that every team has access to the same set of data. For a CDO to be effective and truly let the data make important decisions, every individual needs to believe that the data is unquestionably accurate. From that curated single source of truth, companies can allow smaller, more agile teams to explore specific business needs at depth, trusting that their findings are valid.
The best data teams incorporate a diverse range of inputs
Data teams can provide value to every team at a company, so they should have access to data from every team. More visibility into different aspects of a company means a data team can connect dots that siloed teams would otherwise miss. This also helps business users draw lines between their KPIs and the activities of supporting teams. Since these different teams likely use a range of disparate tools to collect data, it’s vital for data teams to unify these inputs together on a single platform to be analyzed thoroughly.
When it comes to analyzing the data and translating it to business decisions, it’s also important that the process is done by a diverse team to reduce biases. A CDO should prioritize blended team — data scientists, data engineers and domain experts — to look at questions more holistically and draw the lines between individual anecdotes and larger trends to come up with a more comprehensive answer. Once these teams are formed and are regularly discussing the important questions of today, the data scientists can begin to build models that will utilize machine learning and artificial intelligence to start predicting and addressing issues that aren’t apparent yet.
Data teams and decision makers need to speak the same language
An easy way to bridge the gap between business leaders and data teams is to ensure that they are discussing issues from a common perspective. This will ensure that the entire organization is aligned on business goals and is ultimately interested in the same result. If a CDO is going to instill a culture of data-based decisions, it’s imperative that everyone at the company is data literate. This is especially true for the leadership. Similarly, it’s the responsibility of the data team to focus beyond just an individual project and consider the long-term business goals of their organization.
Data teams in general need to make an adjustment to incorporate new voices into collaborative projects. The focus shouldn’t be on the dashboards that they produce, but how actionable that information is to make critical business decisions. As the rate of data literacy at a company rises, these teams can expand access to data across a wider internal population and focus on answering the bigger questions at greater depth using advanced analysis.
Complex data will be analyzed best by small, focused teams
The field of data analysis is evolving to handle significantly more complicated issues. As the questions and the tools change, there is also a need to adapt the structure of the analysis personnel. While a large centralized analytics team has been productive for descriptive analysis, the more complex predictive and prescriptive analysis will be handled best by smaller, more agile cross-functional teams, including citizen data scientists — employees outside the formal data science team who understand the value of data and have the tools to process basic analytical needs without a formal data team.
Gartner’s Carlie Idoine predicts that in the next couple of years, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists. The right response to this increase in citizen data scientists is not just to give them access to self-service tools, it’s to empower them to bring their own expertise to solving critical business issues by allowing them to collaborate in small teams with skilled data analysts. These small teams will have more agility, a higher data baseline and overall an increased ability to tackle complex problems. As data teams move to incorporate more machine learning and AI in their future analysis, it’s crucial to educate these citizen data scientists so there’s companywide clarity around the possibilities and limitations of these technologies.
Overall, it’s an exciting time to be in the data industry. The companies that are going to change the world are using a CDO to align their data teams with multiple departments across their organization and solve more difficult, more valuable issues. As collaboration improves, the stage will be set for a generation of citizen data scientists to step in and use their expertise to perfect analyses that solve specific needs without taking any bandwidth away from data scientists. The use of data across an organization to ensure trust, increase diversity, foster understanding and encourage collaboration will be the difference that sets these companies apart from the competition.