Data will continue to drive business transformations in 2019. Here are three trends that could change the business landscape over the coming year and tips to help you prepare for them. As we prepare for 2019, it's a good time to look back and reflect on the effect that data has had on global business during this past year and what might be in store in the year ahead.
Who Truly Owns That Data?
Data was a coveted asset for businesses during 2018. The mentality of the corporate world has shifted dramatically to focus on making the enterprise data-driven. Coupled with this is the fact that a growing number of businesses have a greater understanding of the utility and value of the cloud and are moving towards putting their data in the cloud -- with governance, security, privacy, and other safeguards in place so that approved stakeholders can have instant access to data as needed from any location.
Compliance was and will continue to be a critical issue. As data, users, applications, and regulations continue to grow, so does the need to stay compliant. Businesses are trying to determine how to ‚??set the data scientist free‚?? while managing compliance. We should expect the GDPR to continue to be a challenge for organizations around the world.
With that in mind, we will likely see the use of data drive even more business transformation in 2019. As we look ahead, here are a few trends that could change the business landscape over the coming year, along with tips to help you prepare for these changes.
Companies are recognizing the benefits of data automation and moving towards automating everything that can be automated within the data lifecycle. There's a disconnect between available resources and the number of projects on IT's plate. Add to that the fact that projects, especially data projects, are becoming more complex, and it's clear that companies are faced with a predicament. Automation helps solve the constraint of a lack of resources.
It's time to stop talking and start doing. Organizations don't need to lay out a full business case and ROI calculation for using analytics and building out their supporting infrastructure before getting started. You won't always know in advance what you might find that could be useful, and you certainly don't know what data quality issues you might have. Make the commitment and move towards automating complex, time-consuming, redundant tasks.
Businesses need to make today's decisions with an eye to the future. For too long, businesses have rolled out IT initiatives only to see their new advancement become outdated on day one. The idea of future proofing, along with the uncertainty of predicting needs for data volume and technology progression, is pushing companies to the cloud. Moving to the cloud also helps companies align their modern toolset with their approach.
Start out small. Develop a lake in the cloud, add a few tools, test, and experiment. Make sure what you design can scale to production. Azure is great for helping you build out a small environment suitable for production in the cloud.
It's essential to update tools but also to update the approach to using data. Users should utilize technology to manage data at every step of the business process. Artificial intelligence (AI) and machine learning (ML) are here and growing, and businesses should come to grips with how to use them to improve their operations.
Data quality is vital to an organization, especially as decisions may be automated through AI. Make sure data quality is accounted for and is a priority to ensure data trust and data integrity.
Achieving Value is the Key
I see these trends emerging because they all provide the potential for great value. We've spent a lot of time during the last 10 years talking about big data and data warehouse modernization, but recently there has been a shift to focus on how data adds value to the business. Because of this, many data and analytics projects that didn't add value were shut down. Now, going to the cloud makes it easier to build a modern data warehouse and data lake, and doing so can provide businesses with substantial value.
Moreover, automation is required to help achieve maximum value from analytics data. In addition, learning new tools takes time, and typically a patchwork of tools is required to manipulate and manage data. Therefore, analysts are now beginning to talk about the value of an integrated toolset. Business users want to ‚??open the tap‚?? and have data flow out, and they're not focused on how the data arrives as long as the platform is able to deliver the data.
Opportunity for Businesses
Besides attaining newfound value, what other ramifications could these trends have on business? The purpose of building out a data estate and the tools on top (AI/ML/BI) is always to help business become more efficient, profitable, and sustainable. Regardless of your role in the business, the data you have is at least as valuable as your products.
This notion is a shift from where we've been over the last decade. In the past, the goal was focused on maximizing internal efficiency. Now, the emphasis is centered around how data can provide company top-line benefits such as selling more products and services, marketing more strategically, building better products, and strengthening customer relationships and loyalty. This, of course, is made possible by developing and considering ‚??what-if‚?? scenarios using data.
What about companies that choose to ignore the opportunity to enhance their data-driven capabilities? They will likely lose out to competitors that respect the value of data. If they lag behind over the next three years, they won't be prepared when AI algorithms are available ‚??off the shelf.‚?? In the end, these companies will likely be the recipients of lower margins, less customer loyalty, and increased competition. Even worse, choosing to maintain the status quo could very well lead to their corporate demise.