Last updated: February 2nd, 2021
Being innovative requires an understanding of the market, technological changes, strong data literacy, and the ability to choose and apply the best fit for your organization. Too often, businesses and technology vendors focus on building up a strong data infrastructure without providing the tools needed to achieve the desired business outcomes.
While data management continues to be essential for any successful BI and analytics initiative, focusing only on and cultivating technical skills will not be enough to stay ahead of the competition. Gartner predicts that, “Through 2022, manual data management tasks will be reduced by 45 percent through the addition of machine learning and automated service-level management. By 2023, AI-enabled automation in data management and integration will reduce the need for IT specialists by 20 percent”1. The ability to automate processes and integrate artificial intelligence (AI) and machine learning (ML) automation within our data landscapes means that the biggest contributions technologists can make to their organizations are through their ability to leverage business and soft skills to complement technical acumen.
Automation and integrated AI and ML will accelerate the time to value of technology use. Without a balanced, business focused approach, companies will get left behind.”
Automation and integrated AI and ML will accelerate the time to value of technology use. Without a balanced, business-focused approach, companies will get left behind. Access to insight is not enough. Organizations and decision-makers need to be data literate in order to make analytics outcomes actionable.
Here is how to integrate business and technology requirements to remain relevant as the market shifts and organizations automate many of their business and data processes.
1. Make Technology Choices Based on Customer Needs
Customers expect seamless service and interactions with companies. With increased automated AI and ML integration within data processes, simply knowing a customer is no longer enough. Understanding how data can add to a customer’s experience can be a sweet spot for an IT specialist and add value within an organization.
2. Understand What Business Value Means to the Organization
The concept of business value might differ for different groups within the organization, but there are clear intersections between the need for strong data access, data literacy, and strategic business outcomes. Understanding how data interrelates and how it can be leveraged is becoming more essential. The need for business and IT cohesion has been a source of confusion for many organizations for years. IT specialists have an opportunity to fill that gap by building business expertise and becoming the bridge between both groups, improving overall organization cohesion.
3. Make Data Relevant
Organizations may find justifying the value of data projects a challenge as initiatives such as data governance can be hard to quantify. The reality is that in a data-driven world with augmented intelligence within business processes, the two are no longer separate. Instead of being the resource or department delivering analytics, IT influencers need to drive data literacy across the organization to help make data a center point of decision-making.
Overall, creating business value requires strong data literacy. Understanding how data is used, what types of data are needed, and how they can be consumed seamlessly within the organization are essential in this new AI/ML-focused, automated service-level reality. Without it, any data investment is an untapped goldmine as very few, if any, quantifiable business outcomes will be realized.
Moving forward, data literacy will trump traditional IT skills (data modeling, data cleansing, data loading, algorithmic design, etc.) because the systems themselves will provide those capabilities to the organization intuitively. Therefore, the value moving forward for organizations and IT-based resources will become the way they interpret and interact with technology and data assets to deliver business value and ensure data literacy throughout the organization.
1 “Magic Quadrant for Data Integration Tools,” Gartner, August 2019