While some may argue that the idea of “Business Intelligence” (BI) dates back to the 1800s, the concept as we know it today really began in the 1980s with the emersion of what became known as data warehouses and decision support systems. Even though there were technological advancements over the next 20 years, the concept of BI changed very little. Business users would typically go to IT and ask for a report when they had a question. Several weeks later IT would come back with some data in the form of a report that often did not answer the question. Either there was a communication gap between the business user and the IT analyst, or enough time had passed that the original question had changed. Sound familiar?
By the mid-2000s there was a paradigm shift taking place, led by many now well-known companies in Silicon Valley. The volume, velocity, and variety of data was transforming how we needed to store and analyze data. Over the last 15 years, big data has exploded in the media industry and new technologies have arisen to wrangle it in and help us illustrate the story it tells. I won’t bore you with the details on the emergence of open source technologies for distributed data storage like Hadoop or new programming languages like Pig, Hive or Python that are typically associated with analyzing “big data”. However this evolution in technology has led us to what we are about to experience – here is what’s trending in BI in 2018:
It feels like we have been talking about the cloud for years now, and that’s exactly what we have been doing – talking about it. Until recently, most industry executives have been wary about putting sensitive company data and information in the cloud. With the barrage of data breaches making headlines in the recent past, cybersecurity concerns are top of mind. Today’s cloud based solutions are quickly evolving to help elevate this angst, and you will see these hybrid architectures pave the way to more mainstream adoption. The cloud brings scalability, speed and mobility, empowering BI professionals to collect, visualize, and transform data into decision making insights faster than ever.
Machine learning and natural language processing are leading us into the future of BI through the automation of data prep, discovery and insight generation. This development has become known as Augmented Analytics and it is taking what were historically time intensive technical analyses and augmenting them in ways that achieve fast, data-driven game changing answers to complex business problems. BI platforms are integrating these advanced analytical techniques to empower business users with statistical tools and algorithms to test hypotheses, validate theories, and confidently predict future results without needing to call in the cavalry of IT staff and data scientists – which leads us to the next topic.
In the October 2012 issue of Harvard Business Review, the Data Scientist was named the “sexiest job of the 21st century”. In 2018, we will see a proliferation of Citizen Data Scientists. Everything mentioned so far – the speed and mobility of the cloud combined with the augmentation of machine learning – is turning mainstream business users into Citizen Data Scientists. The increasing amount of automation in the industry is streamlining the overly technical processing and analysis that previously required a high degree of specialization and programming capabilities. BI is no longer the counter where you place your order and come back to pick it up days or weeks later. With less of a requirement for advanced coding skills, BI is moving towards a self-service model, giving curious business users greater access to data and information than ever before. These are the Citizen Data Scientists, churning out their own analysis, and leading the charge toward next generation BI.
Here at NLD, data is the lifeblood of our organization and modern BI is essential to achieving results for our clients. We continue to invest in upgrading our data analytics architecture and BI platform to drive smart data driven decisions. Our job is to concisely convey a complex story through visualizations that illustrate, in an easy to digest manner, what the data is telling us; to highlight key insights and to make and execute recommendations that improve performance and drive growth. This type of environment creates an army of Citizen Data Scientists, continuously optimizing our clients’ media and stretching their investments even further to work harder for their business.