This is a reproduction of the original article that was published on CIO. The original article can be found here.

The late 2000’s saw the introduction of the mobile application and a supposed new economy. At the same time, the idea emerged that consumers would use mobile applications on their smart phones and tablets to interact with TV shows, movies and commercials. The term was coined “Second Screen.” Multiple companies like Viggle, Zeebox and GetGlue launched and rushed to meet what was perceived to be a demand among consumers for Second Screen applications. Venture capitalist sank money to aid in their pursuits and suddenly there was a glut of Second Screen applications across every store.

Fast-forward just a few short years and the Second Screen phenomenon may be on life support. Few, if any, have successfully monetized it and, as a result, many of the start-ups have gone away. Others have dramatically changed their business models. Second screen is now another cautionary tale – a concept marked by potential, excitement and enthusiasm but not much else.

When I hear the frequency with which the term “Big Data” is bandied about today, I can’t help but reflect on Second Screen. In just a few short years, the term “Big Data” has been coined and abused. Google Analytics reveals that the search frequency of the term increased five-fold since 2012 (see graphic). For me, it prompts the question as to how the burgeoning data analytics community avoids becoming the next Second Screen. How can we prevent the term ‘big data’ from become just another eye-roll prompting buzz term that ultimately peters out.

The answer is fairly simple. Second screen failed because monetization strategies fell short and the strategy did not enhance business performance or shareholder value. For data strategists, the bottom line is the bottom line. Data projects need to align with and contribute to business objectives. Here’s how to ensure that the big data wave doesn’t follow the Second Screen trend:

Not a magic elixir
It’s imperative to understand that Big Data is not going to magically solve all issues inside a business. There is no one size fits all approach and the implementation of a data analytics program will not 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 data can have potential impact. They then need to drive big data initiatives in these directions in a deliberate manner. Big data champions also need to be upfront with their clients and sponsors – internal or external – about the limitations of big data and what all can be achieved realistically and in what time frames.

Get the fit right
The big data path that an organization adopts needs to be aligned with their size, current state-of-affairs and strategic priorities. In some cases, a highly sophisticated and comprehensive approach makes sense. But in others, it might be worthwhile to grab some of the low hanging fruit and pick up some quick wins before making significant investments into a data strategy. Furthermore, too much data can overwhelm and induce frustration and reluctance by team members to embrace and make decisions that are based on data 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 organization. But to extract maximum value from a data analytics program, equal emphasis needs to be placed on investing in the right people as well. Big data strategists need to keep in mind 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 will be the people who will ask the right questions, know which questions can be answered using data and analytics and which can’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 only be maximized if the organization’s teams and systems are prepared and able to leverage the outputs and take action. You might be able to collect data effectively and perform sophisticated analytics to arrive at insights. But if you have not prepared the organization to implement those insights, the actual realizing of value from data initiatives will be minimal and credibility can be lost. I usually see two types of pitfalls here. First, the organization is not data-driven, and there is considerable reluctance among the leadership team to adopt (or even consider) data-driven recommendations. Second, the organization’s systems may not have the flexibility or features to use the strategy and recommendations that come out of big data initiatives. Organizations need to assess themselves on both these dimensions before jumping head on.

Guard and use data smartly
Data project managers need to consider they can very easy alienate customers through their data collection practices and therefore render their data strategy unusable. Security breaches, perceived invasions of privacy, selling of data to third parties and similar activities can erode both internal and external confidence in data analytics programs and send data programs to an early demise. Keep in mind that it is not loss of sensitive data that is a threat – the real threat is loss of trust. So while safeguarding data leakage is critical, it is also important to not overdo big data initiatives such as personalization, targeted messaging, etc., lest your big data programs might be perceived as Big-Brother-is-watching-you programs.

There is much hype and hoopla around big data. But at the end of the day, Big data projects are not unlike any other initiatives deployed within organizations – they must demonstrate value and directly contribute to organizational health to avoid become just another short-lived fad.