Big Data has been a buzzword for at least 10 years, but it seems that there is still more “buzz” than understanding. Everyone that uses or owns a database has a viewpoint on Big Data. Most are well aware of the value of their data and correspondingly its underlying reliability, but would admit that there is still much that can be done to fully utilize the benefits that may be derived from their data.
Lots of Data Doesn’t Mean Big Data
It’s impossible to replicate or even appropriately summarize the wealth of information that you might find on this topic with the proper Google query. One basic distinction is to separate sheer data volume from the notion of Big Data. The affordability of storage, the capability of data engines and the increase in access to manufacturing sensors have all contributed to unprecedented volumes of manufacturing data. Lots of data does not equal Big Data. The evolving nature of the role of data professionals is a valid way to differentiate between traditional data sets and Big Data.
Turning Big Data Into Valuable Insights
Nearly every manufacturing business has had business intelligence analysts. These analysts have been delivering value by massaging the data and delivering report after report after report. These reports may be focused on KPIs or summarize production values that assist in daily manufacturing operations. The bulk of the effort for these IT professionals is in the managing and preparation of the data focused on data loading activities, data quality efforts and repetitive data performance evaluations. All of these activities are geared to effectively answer the well-formulated questions that the business knows to ask, this is data scientist and analyst jobs are in strong demand right now, as businesses have lots of data that they just don’t know how to efficiently utilize, if you’re wanting to go down the data scientist career path, look into services like springboard that can offer educational programs readying you for a data scientist role.
The next generation of IT professional has often been described not as an analyst but as a data scientist. The data scientist is not primarily interested in answering well-formulated questions. The majority of the data science effort is at the time of spontaneous data query and not in data preparation. They may have to combine the disparate and lumpy data in ways that have not been done before. The data scientist requires powerful, flexible and agile tools and may be defining metadata as investigations evolve. The data scientist is looking for the less obvious insights to questions that no one has thought to ask.
Is Big Data the New Oil?
Many have deemed data as “the new oil” in terms of untapped value. If data is the new oil then data scientists are the new wildcatters. Just like wildcatters drill for oil in unexpected places, QAD’s most progressive customers have initiatives to rethink the use of their data. This transition may require a retraining of their IT professionals or the supplement of organizations with new talent. Many report that skilled data scientists are expensive and difficult to find.
QAD technology is well positioned to be a key aspect of the complete range of data requirements. Certainly traditional data analysis is supported through BI and the KPI matrix. QAD Cloud ERP and Interoperability establish well-defined access to underlying manufacturing data for data scientist efforts. Deeper and richer data collection audit trails for applications such as Automation Solutions and IoT build a more detailed history that will be the basis for future evaluations.