The journey companies take to improve their ability to support its customers is a long one. Companies lasting beyond their infancy and achieving financial success are those that quickly learn how important data plays a part. The changing dynamics of risks: wars, wide-sweeping national disasters, and the advances of technology in everyday lives have dramatically impacted the way ‘crunching the numbers’ has matured. The structure and volume of data have greatly changed as well, moving from hand-written ledgers, typewriters and carbon paper copies, rooms of files, punch-card data entry, spreadsheets, to the real-time interactions between systems internal and external to the company. It’s simply incredible how much things have changed on the analytics front in order to compete and profitably grow as a company.
The knowledge, skills, and talent of the workforce have changed as dramatically as the advancements in data collection and analytics of the data. Strong backgrounds in mathematics, business, and technology have positioned the organization to make strong data-driven decisions using descriptive, diagnostic, predictive, and prescriptive analysis. The maturity of the workforce, along with the maturity of technology has led to a convergence that is putting the tools and data into the hands of a broader and more data-literate workforce.
In recent years, many insurance and financial companies have reached an inflection point regarding their data and analytics. Company leaders often stand at the edge of the gap (with great trepidation) assessing their maturity and considering how best to close that gap. As these organizations advance on their analytical journey and maturity, they likely have already made attempts (successfully or not) at implementing various predictive models and price tiering implementations of Enterprise Data Warehouses (common in the 1990s), and implementation of some fashion of business intelligence tools for the executive team and broader knowledge workers. Standing up a core ‘Center of Excellence’ of analytics is a common model that often-followed inconsistencies across various business units attempting to develop their own analytics. The demand for more and deeper analysis continued to grow due to earlier successes in departments and the COEs driven by their success in making a difference to the bottom line, as well as increased industry pressures and competition. Executive dashboards, standardized reports, constrained cubes all meet the needs of an organization that is in its adolescent stages of data consumption. To achieve true maturity, however, companies must ‘free the data’ to enable faster and more insightful analytics and decision-making.
The demand by knowledgeable workers to consume more data has required us to look beyond the traditional model of ‘served’ analytics to one that is more ‘self-service’. Using the analogy of a restaurant, in a more traditional and centralized model of analytics, hungry patrons would choose from a menu of options and place their order. The cook may have already prepared the menu item and it is quickly served up to them. In other cases, a patron may want a special order. The restaurant worker knows where the ingredients are, the preparation required, and cooks up something very satisfying to the patron. This is a typical centralized service that the COEs have provided over the years. However, because of the increased demand and the diversity of analysis needed, the shift towards a self-service model is becoming a necessity. The ‘cooking’ is done by those consuming the analysis and the ingredients are not conveniently located in a single spot.
A new inflection point for companies is how best to enable a self-service model for some, but also serve the larger patrons who are happy with ordering from a pre-defined menu. We need to figure out how to make sure the self-service cooks know where and what the ingredients are. Most companies look across their environment and see warehouses of boxes loaded with ingredients of all kinds with more ingredients coming in on the loading docks every day. Many of the boxes are not labeled, and even some of the containers have missing labels making it difficult for one to know if a white powdery ingredient is flour, salt, sugar, or something else entirely. There is difficulty to know which of the ingredients are fresh and which are long past their expiration date. The knowledge of preparing the meal is critical as well, making sure that all the cooks are using the right ingredients, combining them consistently, and ensuring that the use of their ingredients are going to be palatable to their consumers and not make them sick (bad business decisions). The right amount of guidance by ‘expert chefs’ will always be needed as will the need for another team to have oversight over the quality, labeling, and consistent use of the ingredients.
Moving away from the analogy and back to analytics, analysts need to quickly identify how best to bridge our current gaps as the demand for more self-service continues to grow. This demand includes the use of more internal data at a finer granularity to understand indicators for business operations and strategies. The use of external data, embedded within system processes includes a variety of information from third parties including data from social media, images, and other unstructured sources. The desire for new kinds of data will drive self-service users to include external data into their analyses. The need to bring analytics to more people in the organization and ‘freeing the data’ (as much as allowed) for them to use is a foregone conclusion to stay ahead of the competition. As an organization, we need to enable this as well as put in place appropriate controls to ensure the right access to the right data to provide the best results.
The inflection point many companies are currently at is one that will require significant collaboration from across the organization to set the right direction of our data consumption strategies, ensure we have alignment to execute on that direction, and we have the commitment to see it through to achieve the enterprise goals. Most have been on a very successful journey, but the journey continues. The demand to deliver more data-driven insights to each of our consumers…our employees, agents, and our customers… will continue to increase, and we are preparing to meet that demand. As we focus on advancing our analytic ability and delivering high-quality data faster, we are also focused on improving the data literacy of our consumers so that they can make the best decisions possible.
