Do data warehouses sabotage digital transformation?

At a time when there seems to be tremendous digital disruption, digital transformation in a business requires bold vision and the intention to embrace change. With the global digital transformation market expected to approach $2.8 trillion by 2025, leaders are driving digital transformation in their organizations. And as businesses refine and adapt specific strategies along this journey, they need to understand their data to make informed decisions.

Data-informed decisions are required for digital transformation

The essential understanding of data-driven decisions is because high-quality data is at the heart of all digitization initiatives, from providing invaluable insights to and uncovering strategies. potential performance strategy. And that’s why organizations must be careful in creating data repositories.

Today 73.5% Most leading companies rely on data in their decision making. In almost every organization, data is collected from a variety of sources to analyze and make important business decisions. And while these sources can run into the thousands and millions, integrating data warehouses within an organization is a natural outcome.

While modern databases and repositories are getting more and more powerful, it is difficult for them to completely eliminate data repositories, preventing them from realizing their true potential. digital transformation initiative.

Data silos and other blocks for digital transformation

As a matter of fact, 89% Today’s IT leaders see data warehouses as one of the top obstacles to digital transformation. The formation of silos is often the result of a combination of factors, including mergers and acquisitions, disconnected teams, dynamics between departments, lack of data control, etc.

To prevent the formation of pockets of data between organizations, businesses must cultivate a culture of sharing data rather than owning it. Eliminating silos begins with a cultural shift, which requires a change in perspective that starts at the top of the organizational hierarchy. Businesses can adopt a number of strategies to eliminate data warehouse and prevent them from continuing.

Here’s a list of ways to keep data containers running efficiently

Promoting a data-sharing environment

Different teams within a company hold data close to them, because data is knowledge and knowledge is power. Different verticals often operate with proprietary jargon and processes related to their own departmental goals. Each team finds itself a bit distant and different from the others, and the isolated workspace creates this discordant spirit.

All of this leads to a sense of ownership and reluctance to share data with other groups among groups of individuals, which can harm the larger interests of the organization. Instead, organizations can foster a culture of sharing and facilitate the free flow of information. In doing so, they must also address each group’s concerns about data sharing and ensure a mechanism to maintain data integrity.

Encouraging and motivating groups of individuals to come together and fostering a culture of open data sharing and data consolidation are key to adopting enterprise-wide data connectivity. These initiatives address repositories of data, inspire a positive culture change, turn the wheel of innovation, teamwork and interdisciplinary efforts, and foster innovation. higher cooperation among leaders.

Educating Departments about the Dangers of Silo

Often, the different departments work separately, even when supporting each other to serve a common goal. Companies need to act as a single unit to optimize available data sets and improve teamwork, productivity, and output quality. While enterprise-wide information sharing is key to increasing productivity and creating new opportunities, data silos create barriers to information accessibility, undermining efficiency. overall activity.

Underperformance can make it difficult to spot potential opportunities. Therefore, educating departments about how data repositories jeopardize organizational success is critical to changing the overall approach to data. It is essential to inform teams about the benefits of collaboration and the adverse effects of silos. Promoting information sharing, transparency in task handling, and cross-functional collaboration breaks down silos.

Leaders must encourage group managers to prioritize problem solving and guide the entire organization to ensure a change in perspective. The workforce needs to understand the basics of data silos and what can be done to fix them. They need to be aware of data quality issues that stem from silos. To bridge the knowledge gap, businesses must communicate the benefits of data sharing and data integrity, enabling the workforce to better understand change.

Evaluate the causes behind the creation of Silo

If the challenges of data silos continue to linger, they begin to grow organically, again reflecting on the organization’s work culture. The enterprise setup itself allows silos to build up over time. It happens when every department assembles and accumulates its own data sets, each with its own guidelines, measures, and goals.

Teams working in different departments hone their styles of getting things done or processing data in ways that best suit their requirements. These practices cause silos to build up gradually.

The culture of working separately in different teams creates the problem of silos. In addition, technology and data management system often vary across departments, including tools such as spreadsheets, accounting software, or CRM. Besides, most legacy systems can’t handle information sharing as each solution stores and analyzes data in distinct ways, which naturally pave the way for silos to evolve over time. time.

Data needs constant care and a systematic solution to manage and prevent the easy accumulation of silos. In addition, the best technology businesses have at their disposal can also create inadvertent repositories of data. Businesses that need specialized technology must keep an eye on this aspect.

Establish interdisciplinary teams to monitor

Companies across the globe are now centralizing data and sharing accurate versions of data to save time and cut costs. An enterprise-wide data glossary can be created to provide worldwide guidance on data utility and storage. These data definitions equip interdisciplinary teams with hints on how to understand data, create shared memory, and limit data repositories.

Organizations need to upgrade their digital technology to keep up with changing data.

They need to maintain and evaluate data standards across the entire internal and external ecosystem. It’s important to note that putting all data into a single system will not yield the required results by default. Therefore, companies need to create cross-functional teams to drive the data integration program forward.

Each step must work towards integrating data for the entire business, including different departments, to avoid recreating a new set of silos. It is important to integrate data discipline across all departments and communicate the idea of ​​the innate dynamic nature of data.

Create a route for smooth removal of silos

With the advent of cloud technology, it has become easier and faster to centralize data for analysis. Cloud-based tools streamline data collection into a shared pool, so tasks that once took months and years to complete now take days and hours.

The path to eliminating data silos should include finding ways to centralize data. A central data repository optimized for efficient analysis is the key to finding solutions for data silos. The next thing is to integrate data correctly and efficiently to prevent future data silos.

Organizations can combine data using a number of methods, such as scripting by writing scripts that include SQL, Python, or other languages ​​for transferring data from stored data sources and into the data warehouse. On-premises extract, transform, and load (ETL) tools can also automate the migration of data from various sources to the data warehouse.

Cloud-based ETL is a complex, cloud-enabled, faster and easier process. The process uses a cloud provider’s infrastructure, which works fluently in any environment. ETL tool provides ways to collect data from disparate sources into a centralized location for analysis and silos removal.

They also address data integrity issues by making sure new data is available to everyone. Data centralization consolidates data access and control with data governance framework.

Connected Data Optimize Digital Transformation

Data warehouses negatively impact productivity, insights, and collaboration. But they can stop being a source of trouble when data is centralized and optimized for processing and analysis. When an organization understands the value of having a single golden data warehouse, it changes the inherent culture.

Digital transformation cannot truly take place in an organization without first solving the problem of data warehouses. It takes a lot of effort to address this, including changing culture, assessing short- and long-term tasks, forming interdisciplinary teams, understanding data, and planning for it all to run smoothly. .

While this may seem like a daunting task, in addition to collecting and evaluating data to solve the problem of data silos is instrumental to the success of any engineering transformation journey. which number. It begins as organizations move to a more proactive approach to leveraging the value of connected data.

Featured image credit: Provided by the author; Shutter; Thank you!

Dietmar Rietsch

Dietmar Rietsch

Dietmar Rietsch is the CEO of Pimcore. A serial entrepreneur with a strong sense of innovation, technology and digital transformation. He has been an entrepreneur passionate about designing and realizing exciting digital projects for over 20 years.

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