In a world of increased automation, data-driven decision-making, and capitalizing on the power of advanced technology, data analytics is the strategic focus for many companies today. However, despite the initial promise of data-driven initiatives, these programs lack tangible results, with only 32% of companies using this approach to obtain valuable insights. In addition, the same Accenture survey highlighted that only 27% of the companies who use an enterprise-wide data strategy found success by gaining insight and highly actionable suggestions. These results beg the question and open a conversation surrounding what factors make data-driven decision-making a successful, actionable ruling approach. 

One of the biggest factors around the effectiveness of analytics initiatives is data misinterpretation and reliance. Stefano Puntoni, the director of Psychology at the AI lab at Erasmus University’s Centre for Data Analytics, makes the point that most issues stem from “putting data on a pedestal, but then failing to think critically about how the data was generated and jumping to conclusions.” Arguably the most crucial factor of success, according to Accenture, is looking at data as a key part of the decision-driven data process, which is a strategy that works in reverse of data-driven decision-making mechanism. The key lies in looking at data to achieve a specific objective or answer a particular question rather than leaving it open to interpretation. Other critical capabilities include ensuring data is secure, relevant, and trustworthy, along with having an analytic strategy that augments human efforts with AI by building capabilities in advanced analytics. 

Here at Praesceient, as a cutting-edge analytics firm, we continuously capitalize on the strengths of data while being cognizant of the power surrounding human thought and interaction in order to achieve the most actionable results for our clients. Therefore, I asked Charlie Caris, Praescient’s Director of Services, his thoughts regarding how Praescient ensures that data is handled securely, that data is relevant, and that data is trustworthy. In terms of security, Charlie discussed the importance of establishing “a single point of truth for our data” as it provides a secure way to share and edit mutual documents while avoiding sharing through an unsecured platform, such as email or to a hard drive. Charlie also highlighted that as a company trusted to protect information related to US national security, Praescient consistently maintains high-security standards in all our work, both on-site and off-site. 

Understanding data relevance is critically important to Praescient’s effectiveness as an analytics firm. Charlie believes that it “comes down to what questions our users want to ask of the data and if we can’t answer their questions, then we probably aren’t approaching the data in the right way or looking at the right data.” This mindset directly applies to how Praescient maintains a decision-driven data process that refrains from idolizing data right from the get-go. 

Lastly, Charlie addressed team Praescient’s approach towards data trustworthiness, arguing data should not be put on a pedestal, powerful though it can be in the decision-making process. In order to trust data, we must understand its context, for example how the data was sourced or collected, and by whom. By approaching data this way, Praescient can face the challenges of its clients head-on and use the right types of data to make impactful decisions and analyses. 

Charlie’s words represent Praescient’s approach to data, creating elements of security, relevance, and trust within the company and for its clients. Ultimately, by approaching data from the bottom up, Praescient continues to use data to fuel its decision-driven data process as a tool for empowerment, rather than one that limits company scope and perspective through misinterpretation.