Data Velocity is consistently missing from Big Data Strategies and it’s crippling potential ROI
It is not news that data will increasingly inform and shape businesses of the future. Businesses have already embraced the thinking around Big Data, how it is analyzed and how it is implemented will offer a competitive edge to those that can manage and manipulate it’s increasing volumes.
Why? Insight extracted from day-to-day business could give a window in to possible real life solutions that will deliver competitive edge and, more importantly, increased revenues and profits.
Big Data Strategies and the ability to extract knowledge through complex analytics is becoming a growing concern for enterprises, wishing to have the best chance of staying ahead.
There is no doubt that the top performing businesses of the future will have grasped a better understanding of their business through Data, applying the knowledge it generates – from month to month – in to evolving decisions on processes, operations, products and services around real life business insight. Those that get it right, will have access to accurate, real insight which could also open up solutions to previous unsolved problems that could place you as leaders in the marketplace. Big claims, right?! One thing is clear, Big Data will be the life blood of ensuring a business is running productively, efficiently and with a full bill of health.
It’s why investment in this area of business is rapidly on the rise. Therefore it is not a surprise that investment in these strategies seems set to hit $187bn within the next 3 years.
Big Data teams have 3 main factors to consider when thinking about how to maximise the value of the data the business creates:
1/ They have to ensure they can get data, from where it is created, to where it needs to be for analysis. There then needs to be a plan for how to implement the insight generated from that analysis throughout the business.
2/ Data often needs to be collated from multiple locations, at varying distances from source to collection. So IT infrastructure needs to be adequate to deal efficiently with transferring large volumes over distance.
3/ The data needs to be as fresh as possible so that insight is a a) current and accurate and b) can actioned in time to make an impact on the business. This is where data velocity comes in.
From the conversations I have had with businesses, a lot of focus is being given to the algorithms required for analytics, the process of collection and how the information is processed but not much thought is being given to the data’s journey. Isn’t this putting the cart before the horse?
What is a surprise to me is that very few people seem to be acknowledging the importance that time and data velocity has to Big Data strategies. Or more accurately ‘speed’. What I mean by that is that how getting access to the data as fast as possible, will have a direct impact on how quickly the insight can be harvested and implemented.
Here’s what I think. People aren’t openly discussing how data velocity and speed underpins the whole plan to achieve ROI on Big data strategies because this is the elephant in the room for data teams that no-one really knows how to address. The fact is, as data volumes increase month on month, the challenges associated with getting vast amounts of data to where it needs to be, from multiple business locations near and far, will be the difference between making that valuable insight pay dividends or the whole thing being an expensive mistake.
Next week, more on the Elephant in the Room…
In the meantime, here are some more thoughts about how speed is affecting business data strategy – Click here to read more
data velocity
