Future of Analytics: How to Decide Which Avenue to Take

Analytics can unlock tremendous opportunities in business. Industry leaders today leverage analytics and machine learning across many areas of business, from facilitating customer service interactions to managing logistics to analyzing medical records and even writing music and news stories.

Technology trends, such as cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) have enabled business analytics to become increasingly refined, while supporting the growth of machine learning algorithms that automate the completion of complex operations. This can be a game changer for companies looking to create workflow and operational efficiencies. But how do companies determine which tools or strategies are best for their unique goals? And what will business leaders need to know a decade from now for the continued successful implementation of data management practices?

Data, data everywhere

The world today revolves around data. What makes technology giants like Google, Amazon, and Facebook so successful in this dynamic and sometimes volatile business landscapehas less to do with their ability to be the best search engine, e-commerce, or social-networking site, and everything to do with their ability to harness user data through advanced computing and analytics. 

Devices, apps, and software platforms -- whether for personal or commercial use -- are increasingly being designed to capture data. However, such expansive datasets are often difficult to work with in terms of storage and processing. When you consider the fact that internet users generate about 2.5 quintillion bytes of data each day, the magnitude of opportunity and resulting challenges becomes clear.

Until recently, big data processing challenges were tackled by distributed open-source ecosystems like Apache Hadoop and NoSQL databases like Apache Cassandra. However, these open-source technologies have their own challenges that hinder the progress and profitability of a company’s data and analytics evolution. For example, they require a plethora of manual configurations and troubleshooting that carry a steep learning curve, which can be quite complicated for most companies. This can hinder adoption and make it hard for organizations with an immature data strategy to benefit from the advances these systems can deliver.

For forward-thinking organizations to maximize the value of data analytics in their business strategy, leaders must first identify the current problems and understand potential solutions and roadblocks. Businesses also need to understand the types and location of the data they collect or hope to collect, the sources of that data, where it goes, and how it will be used and protected. This might seem like a tall order, but ultimately, with the right analytics strategy, businesses can leverage large data sets that can be maximized by machine learning or AI in order to save money, time, and improve workflows. Here are three factors to consider when choosing a data analytics strategy:

Embrace the company’s vision. To establish a best-fit data analytics strategy, you must first understand your company’s vision. This allows you to determine which data is most valuable and how it aligns with current resources and business activities. Once you understand the alignment between data and your company vision, you can prioritize which analytics are most important, what your current data architecture will support, and how integrating advanced analytics can support business performance.

Address challenges and maximize opportunities. Each industry has its own unique challenges and opportunities, and your analytics strategy needs to reflect an end goal that allows you to maximize opportunities or address challenges. For example, think about whether your company will lose market share if you do not capitalize on an opportunity and what the cost of inaction is. It’s also a good idea to conduct an accurate and timely assessment of what’s ahead and how corporate goals are evolving, so your team understands how to best use technology like machine learning and AI to achieve success.

Expand data applications. The data you collect and your approach to analyzing and acting on that data will be determined by your goals. It’s common for data scientists to focus on things like customer demographics, financial performance, event activity, and sales metrics. However, don’t be afraid to branch out into other categories of data that can give you more in-depth insights into user behaviors that influence your goals.

Now more than ever, business and IT leaders are relying on analytics and use-case models to better understand customer and user behaviors so they can map out the most direct and efficient path to success. This ability to quickly and securely harness large data sets, to automate tasks with machine learning and AI, and to maximize budgets and anticipate future resources, is a win-win for everyone.

John Affolter is a sales engineer/solutions architect for Charter Solutions, Inc. He can be reached at [email protected].