The late 2000’s saw the introduction of the mobile awarding and a supposed new economic system. At the same time, the idea emerged that consumers would use mobile applications on their smart phones and tablets to collaborate with Tv shows, movies and commercials. The term was coined “Second Screen.” Multiple companies like Viggle, Zeebox and GetGlue launched and rushed to run across what was perceived to exist a demand among consumers for 2nd Screen applications. Venture backer sank money to aid in their pursuits and all of a sudden at that place was a glut of 2nd Screen applications across every store.
Fast-forward but a few brusk years and the Second Screen phenomenon may be on life support. Few, if any, have successfully monetized it and, every bit a result, many of the get-go-ups have gone abroad. Others have dramatically inverse their business models. 2nd screen is now some other cautionary tale – a concept marked past potential, excitement and enthusiasm but non much else.
When I hear the frequency with which the term “Big Information” is bandied near today, I can’t assist simply reflect on Second Screen. In just a few short years, the term “Big Data” has been coined and driveling. Google Analytics reveals that the search frequency of the term increased 5-fold since 2012 (encounter graphic). For me, it prompts the question every bit to how the burgeoning data analytics community avoids becoming the next 2nd Screen. How can we forbid the term ‘big data’ from become merely another eye-roll prompting buzz term that ultimately peters out.
The answer is adequately uncomplicated. Second screen failed because monetization strategies fell short and the strategy did not enhance business performance or shareholder value. For data strategists, the lesser line is the bottom line. Data projects demand to marshal with and contribute to business objectives. Hither’s how to ensure that the big data wave doesn’t follow the Second Screen tendency:
Not a magic elixir
It’s imperative to understand that Big Data is not going to magically solve all problems within a business. There is no one size fits all approach and the implementation of a data analytics program will non cure all prevailing ills. Solution providers and others championing big data initiatives need to identify the specific opportunities and areas inside their business where big information can have potential touch on. They and then need to drive large information initiatives in these directions in a deliberate manner. Big data champions also need to be upfront with their clients and sponsors – internal or external – virtually the limitations of big data and what all can be achieved realistically and in what time frames.
Get the fit correct
The big information path that an organization adopts needs to be aligned with their size, electric current state-of-affairs and strategic priorities. In some cases, a highly sophisticated and comprehensive arroyo makes sense. But in others, information technology might be worthwhile to grab some of the depression hanging fruit and choice upwards some quick wins before making significant investments into a data strategy. Furthermore, too much data can overwhelm and induce frustration and reluctance past team members to embrace and make decisions that are based on information as opposed to gut.
Don’t forget the people
Technology systems and data collection platforms are important and making the right investments in these areas is critical for your system. But to extract maximum value from a data analytics program, equal emphasis needs to be placed on investing in the correct people every bit well. Big data strategists need to keep in heed that data and tools will not lead to insights by themselves. An appropriately sized and qualified team needs to actively spearhead the big data initiative. These volition be the people who will ask the right questions, know which questions tin can be answered using data and analytics and which tin’t, have the right skill sets to develop predictive models, perform segmentation using appropriate tools, convert the insights and models into actionable strategies and then convince the decision makers about their recommendations.
Align related teams and systems
The value and usefulness of a Big Data initiative can simply exist maximized if the organization’s teams and systems are prepared and able to leverage the outputs and take activeness. You might exist able to collect data effectively and perform sophisticated analytics to arrive at insights. But if you have non prepared the organization to implement those insights, the actual realizing of value from data initiatives will be minimal and brownie can be lost. I usually see two types of pitfalls hither. Start, the arrangement is non data-driven, and at that place is considerable reluctance among the leadership team to prefer (or fifty-fifty consider) information-driven recommendations. Second, the system’s systems may non have the flexibility or features to utilise the strategy and recommendations that come out of big information initiatives. Organizations need to assess themselves on both these dimensions before jumping head on.
Baby-sit and employ information smartly
Data project managers demand to consider they can very easy amerce customers through their data drove practices and therefore return their data strategy unusable. Security breaches, perceived invasions of privacy, selling of information to third parties and like activities can erode both internal and external conviction in data analytics programs and send information programs to an early demise. Keep in heed that it is not loss of sensitive information that is a threat – the existent threat is loss of trust. So while safeguarding information leakage is critical, information technology is also important to not overdo big information initiatives such every bit personalization, targeted messaging, etc., lest your big information programs might be perceived equally Large-Brother-is-watching-y’all programs.
In that location is much hype and hoopla around big information. Simply at the end of the day, Big data projects are not different any other initiatives deployed within organizations – they must demonstrate value and directly contribute to organizational health to avoid become merely another short-lived fad.