Yrityksillä on datalle tietokannat, analytiikka ohjelmistot, analytiikkaosaajat sekä halu tehdä dataan perustuvia päätöksiä. Teknologia ja teknologiset taidot eivät kuitenkaan riitä tehostamaan liiketoimintaa ellei analytiikkaa käytetä kokonaisvaltaisesti sekä älykkäästi yli liiketoiminta-alueiden. Analytiikka ja kerätty data tulisi olla osana jokapäiväisissä liiketoimintaprosesseissa.
Harvard Business Review dubbed data scientist “the sexiest job of the 21st century” already four years ago. Big Data may be one of the most hyped business terms that has been thrown around the last few years and according to a Gartner survey, BI/Analytics is the CIOs’ number one technology priority for 2016. It would be difficult, if not impossible, to find a person who wouldn’t want to use data more intelligently and use more sophisticated analyses in decision making. Strategic and tactical decision making is getting more and more data- and analysis-driven – or at least that’s the ambition.
Companies may have the data and they may have a data warehouse. They may even have the robust analytics software and skilled analysts to produce insights and the organizational will to make data-driven decisions. However, without enabling the organization through culture or process to be better equipped to link data and analysis to actual, concrete business processes and actions no value will or can be derived from analytics initiatives. Not only technology and technical skills matter in analytics – at least four softer points of view have to be taken into account too.
Let’s assume a hypothetical case where we have an analytics team (internal or external) and a marketing team at a company. Their common goal is to transform the company’s current marketing approach to become more data- and analytics-driven. But, before they can accomplish that it’s essential to understand the journey of becoming an analytically-driven company. Only then can analytics projects be prioritized and scoped properly with the diligence it deserves. To be even more precise, consider e.g. customer analytics. No analytics-based targeting decisions are relevant to make unless there is an overarching crystal clear view of the big picture: who are our customers; what do they buy and how much; what kind of people they are; is there more potential in them and if so, in which segments; how is the company performing among its current and potential future customers? To summarize, the broader the understanding of the benefits and perspective of how to use analytics within a company, the better it is capable to organize resources to support the mission
Understanding Your Colleague (or Customer)
For the analytics team within a company it’s essential to understand the issues their colleagues are dealing with from a business side. The size of issues vary of course: an Analytics Director might be more involved in top management’s strategic planning whereas an analyst’s counterpart would more likely be a project manager whose tactical actions are be the ones needing analytics and recommendations
Finding the best counterparts might be useful especially in the beginning of analytics journey: some project managers are more data-savvy per se and some analysts have better business understanding than others. It’s easier to start implementing analytical mind-set with the ones most interested in it.
Communication, Communication, Communication
Communication between analytics teams and business functions cannot be highlighted too much. Whether it’s about setting KPI’s or evaluating new concept pilots, active, continuous discussion and sparring are key elements of success. Sales controller’s insights about seasonal sales peaks could be vital for analytics team doing sales modeling which is to determine display ad’s ROI, for example.
Although projects and decisions are most often business functions’ responsibility, analysts have to be committed to projects and take responsibility too: they need to be sure their counterparts have understood their analysis and that recommendations are actionable and well justified. Sometimes this might demand follow-up phone calls or physical visits after an email attached with a PowerPoint has been sent in order to make sure everyone’s up to date. After the actions based on analytics have been carried out and the results been analyzed, everyone involved in the project should be informed about the results. Getting feedback is crucial for both sides to come up with new approaches, more sophisticated and business relevant models and, in the end, better decisions.
Sharing Is Caring
Deepened communication is closely linked to data sharing which is more likely to be in place when functions discuss actively. It’s rare that a company has all analytically relevant data stored in one data warehouse: it’s more often than not spread all over a company’s network locations and hard if not impossible for analysts to find. Lack of relevant data can dramatically decrease the business value of an analysis and therefore all parts involved should be as open minded as possible when sharing data. Data sharing is caring about your common goal.
Organizational and cultural aspects are equally important to take into account as the technical side when becoming an analytically competitive company. As Randy Bean put it in Harvard Business Review in 2/2016: “Companies must take the long view and recognize that businesses cannot successfully adopt Big Data without cultural change”.