Has Business Intelligence Hit the Big Data Wall?
Analytics is becoming a competitive necessity to businesses. Knowing what decisions to make, when to make them, and how to optimize them for maximum impact is the difference between an industry leader or an also ran. Whether the decision is about pricing, packaging (it’s only within the last ten or so years that technology companies have considered the color of the new PC or consumer device), new market penetration, or new products, it’s about making business decisions consistent with well-articulated and understood strategies backed up by data.
Today, many businesses are turning to tried-and-true Business Intelligence (BI) software to solve their analytics challenges. BI is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. As a discipline, BI is made up of several related activities, including data mining, online analytical processing, querying and reporting.
But when it comes to Big Data, is BI hitting a wall? While many traditional BI vendors have moved to adopt clustered data solutions like Hadoop and MapReduce for faster data query, traditional BI methodologies still lack the tools, infrastructure and applications support necessary to sift through massive data warehouses, identify meaningful insights and ignore the noise, and integrate analytics into the actual business processes at the lowest levels of an organization.
Fundamentally missing is an understanding that decisions – whether product related, customer facing or simply business metrics – are made at the top, the bottom and everywhere in between in modern enterprises. Many people maintain the illusion that important decisions still only occur in the C-Level suite. However this is simply no longer true. In the past, empowering executives alone with analytically driven insight was arguably insufficient, but certainly today it’s inarguably insufficient. A data-driven organization must be data-driven from the most senior to the least senior members of the team. This is the frontier of decision management.
At the heart of decision management is rules: rules or thresholds proactively established that structure business goals, risk, promotions, and opportunities. These rules provide the framework for applying analytics. They provide a baseline for all decisions throughout an organization – they ensure consistency in customer engagements, go-to-market activities, financial interactions and virtually every other business related interchange. Then, instead of analyzing data to identify trends, gaps or synergies, analytics professionals can run models based on “if, then” statements within the rules frameworks. This enables every possible decision scenario to be simulated and optimized, and consistently implemented throughout an organization.
The big revolution is in how much easier it is today to integrate these optimized decisions into business applications. By leveraging cloud-based infrastructure and rapid application development tools, developers and data scientists can cut the development, testing and deployment time for customized analytic applications from months to just a few weeks. And, by componentizing the analytics and models, and codifying business engagement rules, development time gets faster the more applications an enterprise develops. By making it easier, less expensive and faster than ever to integrate analytics into business applications the true power and agility of data can be quickly realized by business of any size.
Decision management provides the tools, infrastructure and application support, that BI doesn’t. It enables businesses to integrate analytics into business processes, and get the most out of Big Data. Does this mean that BI won’t make it over the wall? Not alone. To address Big Data challenges, modern enterprises need decision management.