Following from our previous article, “5 Places Where Big Data Projects Failed,” where we looked at five major corporate disasters involving faulty big data deployment, here ‘s a look at the eight key steps to achieve big data success.

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“According to IDC, we are creating more than 58 terabytes of data every second, and we expect to have some 35 zettabytes of digitally stored data by 2020.” (source: Margaret Dawson, Networkworld). With such massive amounts of data in digital space, it is time to ask a simple question, “How to make a Big Data project succeed?”

Eight Steps to Big Data Success

Theoretically it may seem feasible to collect, store and utilize large volumes of data to derive significant insights from refined analytics, but an actual deployment is complex. Big Data may have overcome the limitations of old practices, but implementing it is a major organizational initiative, one that brings both business and IT on a single decision-making platform. It requires complete alignment of the strategy and execution phases. Everyone on board must understand the importance of a big-data centric company culture in driving progress and business success.

Once unlocked, the massive volumes of data can completely transform a company’s business results, ROI and deliver unprecedented business value. Here is a simple checklist of eight steps to ensure big data success in your enterprise:

  • Clarify business objectives and case – Any enterprise looking to adopt a big data system must carefully evaluate their data situation and future data objectives. If the answer to two or more of the following questions is yes, it is time to prepare a business case and get started with big data. The objectives must be driven down from the top and spread across the organization.

Do you need big data? Why

What is the exact requirement?

Are you getting incomplete and inaccurate information from your data?

Are you looking to evolve as a data-driven enterprise?

Is your data affecting your business policy and strategic decisions?

  • Set the Data strategy – Big data can pull accurate, relevant, real-time analytics to generate global growth, but to harness this immense potential, businesses must define a proper strategy. With an objective and goal in mind, it is easier to visualize the path to big data success and business intelligence. Data strategy must be made a part of the company’s business strategy, I.e., relate data initiatives with corporate strategy to maximize its value.
  • Clearly defined Scope – A vast field in itself, big data is not just about extracting large volumes of data from silos. It is also about sifting, organizing and profiling that data into relevant, actionable insights and metrics for the business to rely on. Since big data has the power to directly impact revenue and business growth, a vaguely defined scope can compromise the entire project. However no big data project has ever has its complete scope defined on the first day. The most effective approach to defining scope is the iterative model with a discovery phase, a requirement gathering phase, analysis, and the final solution. Since it is a lengthy process, the progress of the project depends on the commitment of the management.
  • Inter-department consensus – It can be quite difficult to retrieve the analytics-derived information from big data that is necessary for business leaders to compete and thrive unless there is clear communication between the teams handling the data. The business value envisioned by the management must shared across the organization. Communication is the key to a seamless data and analytics integration; it is important to get all the departments on the same page. The final decisions must be unanimous.
  • Management Commitment and Progressive Leadership – Change usually comes from the top management and when an enterprise has a progressive leadership at its helm, it is easier to drive change and promote a data-driven culture. The commitment to a data initiative must run throughout the enterprise; everyone must understand the requirements, the timelines for the project and its importance. Big data can have a major positive impact on business, but it is up to the leaders and the staff to evolve along with the changing data environment.
  • Change Adoption, Legacy, and Authoritative Data Quality – Change adoption is the key to a successful big data project. From a legacy system to the cloud, the journey of an organization rests on how well the change adoption process is undertaken. In most cases, the main reason data marts remain underutilized is because the management or the staff are reluctant to change to a centralized system. Similarly, any big data initiative must be preceded by evaluating the existing authoritative data sources and data present in legacy systems to ensure the right approach to change adoption.
  • Talent and technology – Tools and resources can make or break a project. When data itself is evolving, it makes sense to evolve the skill sets and talent pools. A company that invests in an expert data scientist to extract valuable business insights from their data and a data analyst to understand and deliver that data, is looking at assured success in their big data projects. Similarly, since big data deployments by their very nature are complex, requiring multiple tools and channels to function seamlessly like a cohesive unit, using a legacy tool in a big data environment can compromise the entire system and put data at risk.
  • Look beyond the firewall – Data is not limited to the silos and systems. To get a holistic picture, an organization must look beyond its proxies and firewalls and take into consideration the data from social media, forums and discussions boards, cloud applications and servers, the competitor landscape.

For Big Data to succeed, all that is needed is time and the right opportunity to collect and analyze relevant data. As the world warms up to the emerging power of Internet of Things (there will be a deluge of information from the environment, people and objects) big data will pave the path to success in a competitive market.

In our last article of this series, “5 Times Big Data Succeeded,” we will discuss some of these success stories so remember to log into next week.

Reach out to us for more information on how you can make your Big Data project succeed.