by Debashis Rana –
Knowledge is power.
That’s a commonly accepted truism that has been around for a very long time. In the business world there is only one thing more powerful: the knowledge of what to do with that knowledge. If you were to organize a value chain, it would start with Data, then move to Knowledge, then to Insight, then to Guided Action.
The challenge for companies is this: Just because you have a lot of data, it is not all usable in the value chain. In the age of Big Data, storage of data has been simplified and made more inexpensive. However, making the data usable can still be a challenge.
Banked Data Doesn’t Earn Interest
The term ‘data bank’ has also been around for a while, but you don’t hear it all that much anymore. And that’s a good thing because it’s somewhat of a misleading term.
It implies that data that is socked away in storage is of great value, like money in the bank. But in fact, that banked data is worthless. Unlike stored money, which can at least earn interest income, stored data is of no value.
Data must be used to provide value. And the faster you can put data to work, the more value it provides.
Don’t Bank it; Use it—FAST
Companies can’t approach managing this initial step in the value chain the same as they have in the past. As volumes of data increase and the rate at which companies acquire it accelerate, faster means of managing this data must be employed. That’s exactly what we have made possible. We’ve developed an analyst-driven approach that teams our solution accelerator, RCG|enable® Data Ingestion.
An analyst-driven approach bypasses the need for traditional IT skillsets such as programmers, database administrators, etc.
Massive Time-Savings Potential
How much faster can data be transformed from raw to actionable using this approach? On average, I would estimate an acceleration rate of anywhere from 60% to 75%.
Consider this recent example…
We were building a credit analytics dashboard for an RCG customer: a regional bank. As is typical, we needed to ingest raw source data from multiple systems. The goal was to manipulate the data to take it from raw to actionable by visualizing it on a dashboard. A typical timeline for a project of this type using the traditional approach would be anywhere from 10 to 16 weeks.
We did it in four weeks.
Ancillary Benefits
Cutting out the IT middleman and giving analysts the power to make raw data actionable certainly saves lots of time. But this methodology provides some other direct benefits.
Cost-savings, for example. All of those IT skills that we’re eliminating are expensive skillsets. Eliminating dependency upon those skills provides substantial cost savings. In our bank dashboard example, we completed the project with a smaller team than what a traditional approach would have required.
The ability to turn data into knowledge, knowledge into insight and insight into action is one engine that drives Digital Transformation. More than ever before, making that knowledge easily and quickly usable is the true source of power and the basis of creating value in the new Digital Economy.
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