Rise Above With an Effective Data Management Strategy

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Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Exasol General Manager, North America John Knieriemen offers this commentary on how to rise above the competition with an effective data management strategy.

Many of today’s IT professionals and their organizations are being forced to do more with less in an effort to remain competitive and strategic, placing importance on speed, performance, and flexibility. This is a result of, but not limited to, tighter budgets due to the current economic climate, greater industry competition, and accelerated digital transformation.

When it comes to remaining competitive today, many organizations overlook leveraging the data they already have. In recent years, the amount of data at an organization’s disposal has dramatically increased due to the pandemic and rapid technological advancements. As such, significant decision-making within an organization can and should be supported by its data, making it essential for organizations to have an effective data-driven approach.

Establishing a successful data-driven approach is not just about making faster decisions but also making the right decisions in less time. Organizations that utilize their data to support business decisions can help decision-makers feel confident in their choices and save time in the long run. While it isn’t a sure-fire way to predict the future, data-driven decisions are the best way to predict business outcomes without wasting the time and effort it takes to test out theories or different scenarios.

Building an Effective Data Management Strategy 

For organizations to truly be data-driven, it is imperative that they have an effective data management strategy in place to help them streamline data and analytics capabilities, enabling them to better leverage their data. This can also help organizations prioritize what’s important, minimize business complexities, and set the tone for overall data management within the organization.

The following five tactical steps can help companies successfully become data-driven and determine their own data management strategies in order to remain competitive in their industry.

Break Down Data Silos

Recent data shows that 97 percent of industry leaders believe organization-wide access to data and analytics is critical to the success of their business. However, data silos still run rampant throughout many organizations, isolating information and creating a lack of internal alignment. This can be a sizable problem and lead to issues such as data inconsistency, system errors, misanalysis, and a lack of accuracy, making it difficult for organizations to make the best decisions.

Instead, organizations can encourage increased data democratization and cross-collaboration to ensure alignment across teams. One way to accomplish this is by establishing repeatable processes and methods that allow employees to both analyze and communicate insights from data across departments.

Empower Data Analysts with the Right Tools

Many analytics teams continue to be hindered by analytics tools and resources that do not provide the necessary support and scale for the time-sensitive workloads of today’s size. This can cause data bottlenecks throughout an organization, which can then hinder efficiency by causing them to react to their data, rather than be proactive with real-time data analytics.

Organizations can empower their analytics teams by investing in tools like software-as-a-service (SaaS), cloud infrastructure, or other data solutions that 1) allow analysts to be more effective and productive, and 2) enable organizations to better leverage data across the board.

Incorporate New Technology into Data Processes

Organizations that continue to embrace new technology, including new machine learning (ML) and artificial intelligence (AI) capabilities, can help enhance their data processes and make it easier to sort through available data. For example, merging traditional AI technology – like descriptive or predictive analytics – with newer AI technology like generative AI, can help accelerate these internal processes.

Utilizing these various automation tools can help merge traditional warehouses with modern data service techniques, streamline data analytics, and provide faster data-driven insights.

Transform from Traditional Business Intelligence to Operational BI

Leaders and organizations alike can alter their mindset from traditional business intelligence, which includes primarily leveraging data to create transparency through streamlined KPIs and metrics, to operational business intelligence, which uses data as a driving source for decision-making.

By compiling a thorough data strategy containing goals and priorities, organizations can measure and monitor ROI on an operational basis. This helps ingrain data analytics in the business chain which in turn helps organizations optimize their business operations.

Instill Data Culture Within & Across the Company

Work to incorporate a strong data emphasis across all levels of an organization, regardless of an employee’s technical background or role. This will be most successful if organizations get executive buy-in first, as a company culture centered around data will be greatly impacted by whether the company’s executives are on board.

Once on board, take the time to train executives and employees alike in data democratization and data literacy to help them develop the skills and awareness needed to leverage data across the entire organization.

Despite organizations today having access to more data now than ever before, these data volumes are only going to increase over time, especially in the age of generative AI. Therefore, organizations should work to optimize their data management strategy now to help their company stay competitive in the long run, starting by following these five steps.

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