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Big Data – Round 2

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If you had told me a decade ago that big data was going to get bigger, I may not have believed you – given that most IT terms are often surrounded by so much hype, and given that growing data types and volumes is, in fact, nothing new. Many people thought this ‘fad’ would not last, and were watching its development and adoption with cautious suspicion. However, come today, many top level executives are realising that big data may already have become critical to their business growth.

As I said above, data is not new – but the way in which it is being generated, stored and analysed is definitely getting constantly renewed. And I don’t have to repeat the countless statistics out there to emphasise that it is growing at breakneck speeds to astronomical volumes.

The reality is that with the “rebirth” of technologies like geospatial information and the internet of things (IoT), big data is set to become an even bigger phenomenon in the coming years, as the generations of data are getting larger and larger, and they are arriving faster and faster. But why do I say “rebirth”? Geographical information, for example, has been around for decades, but with apps that allows check-ins on social media platforms and every conceivable device recording its locality, the volume of geospatial data is exploding, and the types of analytics possible on geospatial data is becoming very interesting and relevant. Similarly, machines have been talking to each other since the time modems were first connected to copper telephone wires, but the IoT is taking it quite a few notches up the volume, velocity and veracity scales – even potentially way up on the value scale too. And then, of course, there’s unstructured text – tonnes of it!

However, big data is still being punted mostly by the technology sector, which means it is still seen as an “IT thing” and this also means that many businesses are still not really paying attention to it. Maybe they don’t see the business benefit, or maybe they’re concerned that they cannot master the art of big data. But, given all the information that can be gleaned on customer sentiments, buying patterns, market trends or even on aspects like product life cycles and HR retention – surely the data underlying these insights should be at the top of most successful businesses’ agendas?

The reality is, to make that strategic transition, the C-suite management has to see the value of big data – it’s as simple as that. CIOs and CDOs need to be able to explain to board members, investors and other stakeholders alike, how the data that has been generated and examined can positively influence the day-to-day operations of the business, especially at the bottom line. Let’s face the reality – expanding the ‘data farm’ to a few terabytes or even zettabytes is not worth while if we only retain 3% more of our low-value customers, or optimise the already pretty good delivery routes by a measly 2%. There has to be a compelling business case that illustrates that the value to be gained (or saved) by acting on big data’s analytical insights will be more than the total investment (which includes hardware, software, resources, skills, projects, processes and of course timing). Latching on to previous posts about visualisation and story-telling, we have to make frustrated board members and executives see why big data will be of significant  value to their revenue, and what they may potentially be missing out on.

As such, in the remainder of this post, I wanted to highlight a few reasons as to why I believe big data will continue to be a key trend in 2017 – despite the fact that the conversation around big data has already slowed down. In fact, an article I recently read highlighted some Big Data Predictions for 2016 and having worked through them again, I feel that many of these predictions are actually still very relevant as we head into the new year. (It’s almost ‘New Year’ already – now there’s a scary concept!)

Organisations will need big data talent

With all the effort required to filter out unwanted data and to turn the useful data into valuable insights (especially as businesses find themselves dealing with even more data) organisations will need to employ talented analysts and data scientists, who will be able to focus on the data that the organisation holds and has access to. The data scientist* is, and will still be, one of the hottest jobs throughout next year. The biggest challenge for said data scientists will be to deliver insights in a format that the executive suite can understand and act on.

The future will see machines making decisions, based on big data

In light of the IoT, well the Internet of Everything really, machines will not only talk to each other and record and broadcast every possible data point, but they will start making decisions – many of these based on big data. So here I’m referring to advanced analytical models embedded in operational applications, streaming analytics delivering insights at the same speed as which the real life events happen, and prescriptive analytics recommending the appropriate actions to take.

Data tools will be used more to predict market outcomes

Advanced analytical models and statistical and mathematical algorithms will start creating new and enriched data, information, knowledge and wisdom, which in turn will influence industry trends. Recommendations based on these new insights will affect which services or products clients prefer, and in turn result in new business models being shaped and new customer interaction styles taking precedence.

Concluding remarks

OK, this is an old hobby horse of mine, but I’m not so sure John Mashey or Doug Laney did the technology vendors a favour when they were all lumped together under the “big data” banner, be it with or without capitals. I mean, if you’re the procurement officer in a multi-national law firm, would you rather acquire an unstructured text solution or a (yellow) big data technology (elephant)? Or if you’re in the state’s title deeds or urban planning office, won’t you rather approve the acquisition of a geospatial solution with an underlying graph database or a generic big data technology? As a case in point, most of the NoSQL technologies specialise in a particular category of big data – document databases for unstructured text, value-pair databases for varying data structures, graph-structured databases for networks and relationships, columnar databases for weblogs and other high volume events, and so on.

Anyway, my question is that seeing big data has a great role to play, why do many decision-makers still ignore its potential? My guess is that it is taking the average organisation quite a while to adapt to and embrace the concept of data as a valuable business resource. However, more and more organisations are starting to take big data seriously, and see it as a natural extension of their current data resources. Not only to keep up with technology trends, but also to keep up with their opposition, data science teams and analytics centres of excellence will grow in organisational prominence, and in some organisations will generate insights with enough potential to disrupt existing business models. More big data is coming – round two is the next big thing.

References

https://www.linkedin.com/pulse/big-data-predictions-2016-martyn-jones?trkInfo=VSRPsearchId%3A2069794881476860929632%2CVSRPtargetId%3A6088228878940205056%2CVSRPcmpt%3Aprimary&trk=vsrp_influencer_content_res_name

http://www.computerworld.com/article/3025440/data-analytics/why-data-scientist-is-this-years-hottest-job.html

http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#506b65d55da9

 

 

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