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The quantifiable value of Big Data cannot be denied, especially in this day and age of data-driven strategy and decisions. In fact, for those who know how to sift through it and extract some actionable insights, big data is a veritable gold mine, with great possibility for the smart data scientist who is backed by a data strategy that is completely aligned with the objectives and bottom line of the company. The problem is that while big data may have benefits for all those who leverage it, there are not many who either know how to efficiently leverage it, or how to approach the accumulation of specific data.
According to a report by the Harvard Business Review, over 70 percent of employees enjoy access to data and information that is not for their eyes. Additionally, 80 percent of the time spent by analysts is wasted on determining exactly what they should be looking for. The data discovery process takes more time that could potentially be dedicated to analyzing data. This results in data breaches, since actionable data isn’t achieved in time to set up security systems competent enough to deal with the incoming advanced data.
The external and internal circumstances of the organization are a strong basis upon which the data strategy is to be formed. An enterprise needs to have a clear vision of the expansion scope that they which to accomplish, before they can go about crafting a strategy which will involve leveraging data for organizational goals. A clearly defined purpose will also serve as a metric base which will then define exactly which is the best data for the given circumstances, and how the organization can automate the influx and analysis of said data.
Big data tends to be just as the term suggests – massive and extremely diverse, with a single batch of data containing numbers and metrics which may have a positive impact of several industries at once. To combat this, teams need to be trained to understand the key metrics they require for their goals, regardless of where the data comes from, how diverse it is, and/or what is the data structure. Additionally, investing in a metadata dictionary is necessary.
Big data requires some access guidelines which need to be met, in order to protect it from outside access and malicious breaches. An access and tracking system, custom-designed to match the data that is being received and then relayed, will help in securing the data and tracking how actionable insights are performing.
Enterprise-wide data sharing, while an important part of a good data strategy, is a tricky aspect of it nevertheless. This is because while concerned parties may become more efficient due to the data access, the infrastructure itself gets less secure when non-essential teams and individuals are allowed access to it. For this, an enterprise data management solution can be used, one which provides a robust administration and clearly defined roles to all those who access it.
Administrators can also alter the user roles within the organization, with varying levels of access and privileges. Additionally, on-spot processing is another requirement, one that has to work in tandem with storage and access. Operations and development teams will require data leverage relayed by the data scientists, which would need to be streamlined by a singular system that allows data experts to process the raw data and store it in a location where it can be accessed by concerned teams.
Also important is integrating various data types into a single data packet, and providing operations with a neatly packaged set of potential actionable insights. For this, enterprises must have a system which unifies the diverse data, as well as enforces adoption of analytics and taxonomy, and data referencing.
Any big data strategy is incomplete without the aforementioned elements, as they represent the major needs of not just the strategy itself, but the enterprise that hopes to use the influx of data to their advantage. Data science training can provide managers with the opportunity to equip their ops, development and data teams with all the required tools to not just create an effective big data strategy, but to keep it running seamlessly as well.
Additionally, with the advancements in existing technology and organizational standards, superior trained teams will be the key differentiator between a successful organization and a struggling one. Implement data science training sooner rather than later, to make sure that your enterprise is ready to tackle the big data challenges of tomorrow.
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