Preparing your data for analytics

As more and more companies are looking to take advantage of the intelligence inherent in analytics, many wonder how best to get started when it comes to preparing data for analytics. The requirement to find, clean and organize corporate data can fall upon the data analyst. Rather than actually spending time interpreting analytics and trying to solve problems, determining answers to questions posed or predicting emerging trends, data analysts can often find themselves spending the majority of their time simply trying to prepare the data for analytics.

With that in mind, let’s look at how an organization might go about preparing their data so that it’s fully operational for an analytical environment.

Begin with the end in mind. It may sound counterintuitive for those wanting to prepare their data for analytics that they consider the ending before the beginning, but that is indeed the case when trying to develop a plan to manage your data for analytics. Architects and users should step back and first consider the future.

With the lightning speed at which technology advances, a solution implementation today can be out of date tomorrow. Having an endgame in sight is critical to help save time, money and human resources. One of those important factors to be considered for the future is the deployment of your data to the cloud.

With studies showing that the amount of global data doubles every two years, having an infrastructure in place that can scale with this massive volume of existing and future information is imperative. This proactive measure is known as future proofing, and for good reason.

With a cloud deployment, companies can allow access for approved users from any location at any time. Businesses can take advantage of modern toolsets and pay for only the software, data storage and processing power that they need and use. Downtime is reduced as is operating costs for hardware systems, and the investment in IT hardware is eliminated.

To start with a cloud deployment for enterprise data, organizations should begin with a data lake in the cloud. From there, they can add a few primary tools to experiment with. While starting small is a smart strategy, it’s essential to build a design that can scale to production, thereby helping to ensure the future-proof platform.

Consider a layered approach. Companies typically have different types of users who need access to data. With those different users come different needs and requirements that call for different forms of data. For instance, a data scientist might want to have data in its raw form that has not been cleansed, while a data analyst might want data that has been consolidated, rationalized and cleansed. Business users usually depend on data visualization tools that rely on governed semantic models with documented data sets. To achieve all of this, we recommend what is known as a layered data architecture.

With this layered approach, a data lake is created from various data sources. From there, one or more data warehouses are built. At this point, semantic models are produced either from the original data lake or the data warehouses. Then, users can enjoy and benefit from self-service analytics. The layered approach also provides another crucial and significant benefit: All users are analyzing the same data, which leads to providing “one version of the truth” across the enterprise.

Use a meta-data method. Analytics data should be extracted from data sources and operational systems. Traditionally, this has been accomplished by writing code, but writing code is very time consuming and can lead to errors and a lack of documentation. The time required to code has caused a lot of internal pain for organizations. By using what is known as a meta-data-driven methodology, businesses can automate this process by auto-generating code to extract data from source systems and then populate analytics repositories, while maintaining security information and documentation where the data originated from.

Look to automate. Automation doesn’t just help with the meta-data-driven methodology – it helps in other ways and should be leveraged. Simply put, automation can transform the analytics lifecycle, from gathering raw data to suggesting insights. Utilizing the power of automation frees up IT staff to work on more strategic initiatives that require human intelligence rather than spending myriad hours on time-consuming, manual and redundant tasks.

Account for governance and compliance. Last but certainly not least is the importance of data governance. You’ll want to ensure that your data management platform and analytics environment is in compliance for regulations such as GDPR, HIPAA and Sarbanes Oxley. You’ll want to build for privacy, security and access control, and you’ll want to document your data and provide access rules in meta data (data about your data) as stored in data lakes and data warehouses. The layered approach that was mentioned earlier helps to simplify structure for all these governing components.

There are many ways to prepare your data for analytics. Of significant importance is the need to ensure that IT and business users talk and collaborate on the data analytics initiative. In addition, technical specialists who understand governance and compliance should be part of the project team. The data collection and preparation efforts for analytics should be a mission that cuts across all functional departments throughout the corporation. While the goal is instant access to and democratization of analytics data, it cannot come at the expense of governance.

Generally speaking, many of the methods currently being used by organizations are way too complicated, time consuming and costly. What we’ve laid out here is a proven outline and overview for preparing your data in pursuit of having a self-service, analytical enterprise that efficiently helps you take advantage of analytics today, while also having an eye on an analytics data platform that can do the same tomorrow.