There’s no denying savvy company leaders rely on data for optimal decision-making in nearly all aspects of their business. From consumer preferences to industry trends and future forecasts, smart data is taking business intelligence (BI) and interactive analysis to “sophisticated” new levels. But buyer beware: Not all data is smart data.
One could compare effective data analytics to sifting sugar. If you don’t sift your powdered sugar, you end up with lumps in your frosting. If you don’t sift your data, it “clumps” bad data with good data, giving you poor analytic results. By sifting all your data first, you can put only the good data into your program, avoiding clumps to get the results you need. While the rise of data discovery and access to multistructured data are disrupting the traditional throw-it-all-in model, sifting through and determining the most meaningful data still presents problems for many. According to the press release, Gartner Research Vice President Rita Sallam stated, “Data preparation is one of [the] most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms.”
Existing business data analytics models face a common problem: analytic sprawl, “an inconsistent or incomplete use of data, capricious development of metrics and formulae, and either too-restrained or unrestrained sharing of results.” And with company leaders facing information overloads, they face greater risks for drowning in a sea of uselessness and noise, overlooking what’s really important. Efficiently utilizing data without falling victim to these problems requires business leaders to identify two key factors: what information they need and how to capture it.
The definitions of smart or useful data types can vary based on company needs. However, many methods for determining important data are universal. Business analysts should consider what insights and information are most valuable, where or which systems store them, and why any existing analytics models have hindered data collection thus far.
For instance, ask what information will help pinpoint customer retention factors or otherwise further business goals and objectives. Analysts also recommend developing a maintenance plan. As analytic tools improve and standards change, analysts must equip systems that handle big data to adapt to those changes.
Business analysts should also consider wide data. Wide data derives information from more than just traditional sources. It may incorporate social media information, customer behavior, and IoT data.
Although certain solutions are one-size-fits-all, it’s important to keep in mind that changes analysts make to existing systems can also present limitations. Reconfiguration can be costly, especially when accounting for analytics training: Separated and scrutinized databases can cause latency, and certain analyses can slow down systems. In short, growing data types and uses have forced professionals to fundamentally rethink how they handle their data.
Once company leaders understand what information they need and identify any limitations that may develop, their challenge becomes information acquisition. Luckily, according to the Gartner press release, the growing number of smart data discovery tools company leaders can use to obtain data are also getting smarter, helping users better identify key information while reducing complexity and time-consuming analysis. Analytic strategies must align with business strategies, meaning the process requires consistent communication between all parties involved.
One key process for handling big data is de-duplication, the identification and removal of duplicate records from a data set. Although removing redundant data may seem like an obvious step, it is perhaps one of the most effective ways to reduce the amount of data systems must sort through. According to the IoT Agenda article, another key step is to understand and properly sort structured and unstructured data as data arrangements are integral to those using different system types. Some systems are powerful for handling unstructured data, especially with some structured data, but not for analytics. Machine learning-enabled programs are proving very useful for sorting data and analytics. For instance, these programs can not only sift through data but can also offer valuable insights such as patterns and statistics to help analyze the data quicker and more efficiently.
The smarter your business intelligence is, the greater your ability to make savvy business decisions. Don’t assume all your data is smart. Be mindful in the ways you collect, analyze, transform and implement your actionable information — and ditch the rest.