To unlock the value of their data, organizations need to capitalize more fully on data analytics and artificial intelligence to drive business insights. Here are some tips on getting started.

In today’s enterprise environments, data is being created, captured and consumed at unprecedented rates. With this data explosion comes the need for new, highly scalable platforms for data collection, management and analysis. The ultimate goal is to use data analytics and artificial intelligence to unlock the business value inherent in data, by taking a strategic approach to capitalizing on data.

Business leaders understand the opportunity brought by massive amounts of data, as well as the new imperatives for digital transformation. Consider these findings from a Dell Technologies-commissioned survey of thousands of business leaders around the world:

• 80 percent of organizations have fast-tracked digital transformation programs
• 79 percent are reinventing their business models
• 89 percent recognize that as a result of recent disruption, they need a more agile and scalable IT infrastructure[1]

To successfully navigate this new era, organizations need to transform not only business processes but also the environment upon which the business is built — from the edge to the core to the cloud. Data analytics and AI are at the core of these initiatives, and there are many opportunities for starting on this journey.

The path to data analytics and AI

So how do you get started down the road to greater use of data analytics? One way forward is to follow a path that spans from strategic planning to solution deployment, just as you would for any major IT project. With that though in mind, here is a brief look at one possible path to making greater use of data analytics and AI in your digitally driven enterprise.

Planning for the journey

Building a data-driven organization using data analytics and AI to create insights is a journey. Before your organization starts down this path, it is essential that you gain a clear view of the road ahead, along with a clear plan of action for what you want to accomplish with your data.

In particular, the action plan should cover three critical phases in the data analytics journey:

• Consolidating data with solutions such as data lakes
• Using data analytics to drive operational efficiencies and business transformation
• Enhancing the organization’s ability to act on data by building cloud-native applications

Beginning the journey

For organizations working to consolidate data to extract value from mountains of structured, semi-structured and unstructured data, the next step is to put the right data platforms in place.

For example, Apache™ Hadoop® offers compelling benefits for data storage and processing. In a big advantage, Hadoop can store any kind of data in its native format, from any source, cost effectively and at very large scale, and it can do sophisticated analysis and transformations of that data easily and quickly.

Consolidating data for analytics

Many organizations struggle with this step — either to begin the data analytics journey or to make data consolidation projects successful once they’ve begun. Organizations are often impeded by a lack of Hadoop expertise, and end up spending too much time and effort on the front-end work before they can get to the results of a fully operational solution.

To avoid these pitfalls, it’s important to partner with organizations that offer both expertise and infrastructure for data consolidation. Dell Technologies is ideally suited for this role. It has teamed up with industry leaders — including Cloudera and Intel — to help organizations remove the uncertainty and barriers that may impede the deployment of a consolidated data analytics environment.

Driving digital transformation with data analytics and AI

There are two primary categories of use cases for organizations on the path to data analytics and AI: improving operational efficiency and transforming the business.

Operational use cases include data warehouse optimization, data repository consolidation, data exploration, data analytics and active archives. Business transformation use cases include risk modeling, product improvements, customer loyalty analysis and customer attrition analysis.

The important point here is to identify the data-driven use cases that are essential for your organization, and then make them a priority for your data analytics journey.

Key takeaways

To compete effectively in the new digital era, organizations need to transform people, processes and the IT environment upon which the business is built — from the edge to the core to the cloud. Data analytics and AI are at the heart of this transformation, and there are many opportunities for starting on this journey.

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Source:  CIO