The average age of a company listed on the S&P 500 has fallen from almost 60 years old in the 1950s to less than 20 years old today. Innovative companies that are willing to embrace transformative technologies make the list today, while businesses that are hesitant to embrace change risk becoming obsolete.
Thriving companies, innovators, value their data as an asset. They use big data solutions as a competitive advantage to increase revenue, reduce cost, and improve cash flow. Data is woven into the fabric of every organization. It records what happened, but increasingly, it’s being used to drive change and transformation at unprecedented rates.
Any business leader looking to maximize their data needs to ask themselves: Does your organization have a comprehensive data strategy? Does that strategy address both structured and unstructured data? Do you have a platform that allows your organization to analyze transactional data and social sentiment?
If you answered “No” to any of these questions, chances are you have untapped data resources or, at the very least, under-utilized data resources.
Experts claim that there is a 10x return on investment in analytics. For some organizations, that’s the low end estimate of value they’ve created. Industry analyst firm IDC has even estimated there is a $430B economic advantage to organizations that analyze all data and deliver actionable insights. The bottom line is that the opportunity is big, and growing.
How valuable are your Data Assets?
Data has been doubling every couple of years for a while now. With the exponential rate of growth in data volume and data types, traditional data warehouse architecture cannot solve today’s business analytics problems. You need new approaches to handle the growing complexity while trying to maintain expenses and stay ahead of the competition.
Your customers, channels and competitors are digital. So are your employees and increasingly even your products. Digital transformation is critical and according to Forrester, 89% of executives see it impacting their business in the upcoming 12 months – and that survey was taken in 2017!
Also, machine learning is more than just a buzzword. It’s a core part of the solution. For many, even most companies, it’s the most important part of the solution. With the arrival of big data the data itself it quite complex as are the interactions between different data sets or types of data. Machine learning algorithms are able at some level to figure things out for themselves.
The point is that there are new types of business challenges that organizations are facing today. To get the most return from your organization’s data capital, you need to be well versed in transformative technologies that are available today and approaches that you can use to reduce cost and yield valuable business insights. You should plan to invest more in advanced analytics tools to get the most value out of big data that you continue to accumulate over time.
What steps are Organizations taking?
Organizations are building a modern analytics platform, they are demanding access to all the data they need. Data to inform every decision, when and where it matters. They want to rely on a modern algorithm to crunch their data. Pretty much any ML algorithm, is likely to give better or more accurate results when there’s more data to work with. Whether you are trying to build a better view of your customers’ wants and needs, or figure out why a component is breaking, you’ve got to start with as much data as possible.
Data science is becoming a key part in enabling organizations capitalize on their data. Companies are looking to use data science, and to figure out how to incorporate it into their businesses.
Finally, they want all the data and all of these algorithms and this modern technology to be put to work in support of the applications that are used to run their business. For example, if you look at Oracle’s Adaptive Intelligent Applications, it combines Artificial Intelligence, machine learning, and decision science with data captured from Oracle SaaS applications and third-party data. The unique value of these learning-enabled applications is that they learn from results, which increases their accuracy as they are used over time.
