What is next in Big Data?
A Shakespearian line would probably work well here. Let us pretend I dropped in the line and get right to it.
The Big Data technology vendor race will continue to consolidate. NoSQL and Hadoop will grab the masses while the other players evolve into niche solutions.
Campaigns will continue their journey to a fully data-driven design with the near-term goal of working out personalisation along with further drives on reducing costs — generally through ultra-targeted campaign efficiency.
Longer term, these ultra-targeted campaigns will run their course of effectiveness and marketers will have to loosen their focus to bring in new customers from the fringe groups. Think in terms of low-hanging fruit. Eventually you have to climb the tree or starve.
Mining through the available data points of targeting reaches the point where you just can’t attract customers without changing something. After some soul searching, everyone will realise that there is truth to the axiom that the list works in combination with an offer and product — the three stand together. So, you will need to change your product, price, message, or campaign creative to continue to expand.
All products reach a saturation point. Not even indoor plumbing is in 100% of the nation’s homes.
The art of marketing is to push (sell) your product to get to your own saturation point. Then keep it as high as possible for as long as possible while maintaining a sustainable cost-to-volume equilibrium. These balance points are moving in the publishing industry.
Learning how to use all possible tools to understand and predict these changing points is another place where the marketing space is changing. Reporting dashboard tools like Tableau, Cognos, and others in the category are critical to understanding, visualising, and communicating what is happening in your market space.
Technology in general
Moving technology to the right priority level in your marketing efforts will become critical to the staffing (skill sets) of the operation. Don’t get trapped by thinking that technology is the answer. It is one of several ways to push the art of marketing.
The job of technology is to help understand what makes a product move in a market as well as the way to accelerate the purchase by reminding or signaling to people that there is something they just can’t live without.
Big Data, round two
Time is coming to prove the value; the “so what” moment has arrived. The days of being a “player” simply by patting yourself or your organisation on the back by having a 387TB Hadoop database on 40 nodes sitting in the cloud somewhere in the desert of Nevada and a data scientist are over.
The C-suites are starting to look for the return. We are now into the “so what does it get me” phase. The investment was made. Now it is time for the payoff.
Hardware and analysis tools
On the hardware front, the sheer horsepower of the Big Data appliances is staggering. Solid state storage is catching up as well. The evolution from white-box, commodity-level hardware to high-end, purpose-built machines continues, but is the need still there to do the Big Data hardware infrastructure as white-boxes?
The big players (IBM, HP, Dell, and so on) have highly tuned appliances all pre-packaged with the management tools. The evolution continues to move to pre-packaged from the build-it-yourself with spare or re-used machines. Amazon, Google, and others have cloud solutions that are also viable and a better use of your scarce IT resources (versus a build-it-yourself plan).
On the software front, the evolution of the tools to simplify the analysis and for the presentation of the analytical work continues. A few years ago, the data scientist was the solution — someone who was 25% programmer, 25% statistician, and the rest marketer, presentation designer, explorer, and dreamer.
Now the presentation and analytical software tools are catching up so you don’t have to find the few remaining and affordable data scientists to move forward.
Or put another way, many of the old tasks of the data scientist role are now software-enabled rather than Python-scripted. This will change the role of data scientists, and free their time for more solution-finding rather than cutting code. There is still a long path to walk, but the pathway is starting to form.
That is enough to think about for now. So let us all get ready for the NoSQL takeover, Apache Spark, the call for Hadoop projects to deliver, solid state-based server storage, and fast access through smarter analytical tools.