By Steve Cavolick
Happy New Year! Sure it’s a little late, but the first month of a new calendar year is the perfect time for self-evaluation and goal setting. Digital transformation is occurring at record pace, and fueling that transformation is the ability to source, cleanse, enrich and govern data.
These are the trends in 2022 we think you should be watching and thinking about how to best incorporate them into your business strategy.
The Need For Speed
Do you have an enterprise data strategy? If not, what are you waiting for? We know that having a data strategy to support the business strategy can validate that the right processes and operations are being used. This survey shows that companies who have had a data strategy in place for at least one year have higher profit growth than those with newer strategies or those who plan to implement one in the coming year. When you operationalize your data strategy, you will see financial benefits.
Data Governance Matters More Than Ever/ Year of the Data Fabric
Organizations use data to fuel growth. As organizations grow, the volume and types of data needed to answer questions and make more accurate predictions also increases. When you bring in more new data, there are new privacy concerns, not only for regulatory compliance, but also to build trust with customers. Having a data fabric with ML-driven processes to constantly scan your data sources and automatically classify sensitive information lets you confidently deploy a data storefront for analytics users and enable MLOps for data scientists.
Data Sharing Is Essential
The benefits of combining your data with external sources range from understanding your customers better for hyper-personalized offers to understanding supply chain events for nowcasting vs. forecasting. Ditch ftp and Excel data integration for cloud data sharing and centralize the acquisition of outside data through the office of the CDO.
Data Engineer Becomes The Coolest Job On The Planet (Sorry Data Scientists)
According to a Gartner study, 85% of AI projects fail. Two of the biggest reasons are lack of data science skills and unprepared data. Data Scientists currently spend too much of their time preparing data to train and feed algorithms. Missing, incorrect, duplicate, and outdated data may lead your data science team to frustration and failure. This is where the Data Engineer steps in. Data Engineers will have more SQL skills than a business analyst and use ELT approaches to build automated, repeatable data pipelines. They will also apply software engineering best practices (think version control and testing) to enable business users and data scientists with trusted data they need to answer questions and build AI models.
Sustainability Becomes A Major Pillar Of Corporate Strategy
The ESG movement is upon us and companies are taking a closer look at how they track and prove their impact on the environment. For example, a clothing retailer may commit to using less plastic bags or reducing lightbulbs in stores, but environmental impact is going to be minimal. In order to really make a change, companies must look at their entire supply chain, from raw materials gathering and processing to finished goods. This means you will need to collect data from suppliers, contractors, transporters, and vendors. Improvement can’t begin until you can create a baseline for energy consumption or water use. This is just another reason organizations will need to take a data-first approach to decision making.
If you are interested in learning more about how LRS can help you find value in your data through strategic roadmapping and implementing advanced analytical applications, please fill out the form below to request a meeting.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.