After migrating its analytics to IBM Watson, a UK company's IT team gets data reports much faster, allowing them to more actively monitor operations against KPIs.
While companies have been beefing up data analytics staffs and calling upon universities to churn out more data scientists, another front of activity has quietly emerged that exposes many of the old cracks in enterprises that historically have stood in the way of getting work done. Simply put, how do you get everyone in the organization to agree on what the business challenges are and how to address them?
On the surface, these challenges might seem straightforward, but they're not. One reason why is the painstaking strategic consensus building process that often transpires in large enterprises.
Do you tell HR or an IT executive that their staffs have to remain flat so that more salespersons can be added to improve revenues? And then, there's the manufacturing executive who tells you that, even if orders were to improve, there wouldn't be the capacity to process all those new orders without a sizable investment in a new production facility.
Standard company reporting that uses system of records (SOR) data doesn't always answer these questions. Consequently, executives hope that big data will. This places the spotlight on the business data analyst, who understands the business and the data, and is tasked to find answers in big data that can resolve internal political battles and get everyone on the same strategic page.
IBM Watson solves three analytics objectives for one company
This past week, Adam Hunt, business systems manager at Mears Group PLC, spoke in an IBM analysts presentation about the importance of moving big data into reports that are actionable. "To do this, reports must use a single source of data and be able to be used by virtually anyone in the end business," he said. Access to big data is essential to Mears, which delivers social housing repairs and maintenance services in the UK, and also has a complementary business line that provides 150,000 care hours per week.
With over 20,000 employees at headquarters and in the field, Hunt said that it was essential to get a handle on more than 600 sets of events data so the organization could actively monitor its operational performance against key performance indicators (KPIs). In the past, managers were challenged to do this, because it was taking IT six to eight weeks to incorporate new data sources into the reports managers were requesting.
Mears made a decision to move to IBM Watson, where report turnaround was reduced from weeks to minutes, with everyone using a single source of data, and with users having the ability to drop this data into spreadsheets, bar charts, and other reporting vehicles that they were already comfortable using. IBM Watson even prompted users to ask questions about the business they hadn't considered, which led users to discover new insights and revenue opportunities.
In making the move to a new analytics platform, Mears accomplished several objectives:
- it eliminated the wait times for IT to produce reports for a business that was rapidly moving, so users could take action sooner;
- it consolidated myriad data sources into a single data repository so that everyone was using the same data, enabling executives to get on the same page sooner; and
- users now had the ability to develop the analytics reports that they wanted, using spreadsheet and chart technology that were familiar to them.
Is IBM Watson a sign of what's to come?
It's likely that analytics systems like IBM Watson will continue to gain traction in enterprises. There isn't time for most companies to wait for the right data analyst or scientist to come along, or to develop all of the big data algorithms by hand.
Systems like IBM Watson -- that not only enable users to take control of their reporting, but that lead them into new algorithms and queries in a user-friendly manner -- can get enterprises to bottom line answers sooner.
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