August 18, 2022
10 minutes read
Information Insight

Data Silos: Why They’re Problematic and How to Break Them

Diane is the head of sales for a financial services firm that wants to target more high-value clients and build deeper relationships with its most successful accounts. To create the strategy to support this goal, Diane needs to build a comprehensive client profile. To gather the information she needs, she finds herself pulling data from multiple sources. Her CRM and sales enablement tools contain some customer intelligence, but they’re incomplete. To paint a more complete picture of the company’s clients, she schedules multiple meetings with her colleagues from marketing, account management, and finance. She then compiles all of the data she’s gathered into complicated spreadsheets and slide decks. It’s time-consuming for Diane and her colleagues, and she’s still not fully confident in all of the information she pulls together. 

Does this scenario sound familiar?

The problem is siloed data. All of the information Diane needs to inform her strategy is available, but it’s held in disparate systems, owned by different teams. Even when those teams communicate regularly, it can be difficult to extract and share data that’s actually meaningful. Data silos create a ripple effect across organizations, and breaking them down can be a critical first step to improving productivity, revenue, and customer loyalty. 

What Are Data Silos, and Why Are They Problematic?

Data silos are isolated information sources that are owned by one department and are inaccessible to others in the same organization. Siloed data has wide-ranging negative implications. Employees like Diane spend a significant portion of their time looking for information instead of performing more productive tasks. In fact, more than 50% of office professionals say they spend more of their time looking for files instead of doing their actual work. 

For organizations with siloed data, addressing this issue can often lead to a never-ending data cleanup process, where resources are devoted to fixing the symptom instead of the cause. Processes are slow since many tasks require manually compiling data from multiple sources, later leaving employees with the looming task of figuring out how to aggregate all the data all into one place to create a single source of truth. Company culture can also be a casualty in the battle against bad data, causing friction and frustration between departments when trying to gather all the different data sources together.

These issues can lead to serious consequences for companies in heavily-regulated industries like healthcare and financial services. And they make it hard to innovate, which is vital to remaining competitive in a fast-changing business environment. 

Tools Help Break Down Data Silos 

Data warehouses are used to centralize data and prevent the problems caused by silos. Some of the most popular data warehousing tools are Microsoft Azure Synapse Analytics, Amazon AWS Redshift, Snowflake, and Google Cloud Platform BigQuery. Using these tools as a single source of truth can be an important step toward breaking down data silos. Creating a single source of truth for all data points harnessed by a company is the holy grail when finding remedies to disparate data sources. Providing a full, 360-view into a company and its clients/customers for employees to work with creates an opportunity for a more insightful and digestible comprehension of how and why growth can be achieved. 

For example, with a single source of truth, it’s easier to create insightful dashboards and graphs in data visualization tools like Power BI and Tableau, making communication across departments and with leadership more seamless and intuitive. It’s also easier to gain better context around your data, leading to a deeper understanding of the problem and solution at hand.

But Tools Alone Can’t Solve the Problem

Using the right tools is an important aspect of closing data silos, but they aren’t an instant fix. To close data silos, data warehousing tools must be leveraged as part of a broader approach to data governance, architecture, and management. This requires a more methodical approach:

  1. Create a data model 
  2. Ensure various data sources know how to speak to one another
  3. Derive insights from a single point of origin

Taking these steps can help you do more with data, from improving the quality of your dashboards to implementing next-gen tools like machine learning and artificial intelligence. But in order to get there, it’s important to take an incremental approach, which we at Kenway like to call “Crawl, Walk, Run.”

Crawl, Walk, Run: Taking a Practical Approach to Breaking Down Silos

The Crawl, Walk, Run approach is all about setting the right foundation. Instead of focusing on achieving the desired end state immediately, we encourage organizations to focus on increasing data maturity in increments. Oftentimes, consultants will agree to help a client reach their desired goal (such as implementing advanced analytics or AI), but neglect to assess whether the client is ready to achieve it, and if the goal is what they need right now. At Kenway, our approach is to be practical and honest with our clients, which allows them to actually realize their short-term goals and be successful in the long term. 

  1. Crawl

At this stage, organizations are using Excel for analytics, reporting is done on an ad hoc basis, and data is limited. We help companies crawl by setting up more robust data warehousing and visualization tools. Companies at this stage can begin the process of upskilling their employees and identifying the capabilities they need to take data management to the next level.

  1. Walk

Organizations that are ready to walk have gained better institutional knowledge around data management. At this point, we introduce a full analytics suite so they can access insight across multiple data sources. Dashboards can be created in minutes instead of days or weeks, making them more valuable. Organizations at this stage are also ready to introduce self-service analytics. 

  1. Run

When an organization is running, they consistently practice good data governance, approach master data management effectively, and have high-quality data as well as a solid data infrastructure. Their change management practices are effective, ensuring that data and processes remain intact even as the organization evolves. It isn’t until this stage that companies are ready for leading-edge applications like machine learning and artificial intelligence. 

Best Practices for Removing Data Silos and Improving Business Intelligence

Whether you’re ready to crawl, walk, or run, the following best practices are essential to removing data silos and keeping them at bay. 

  1. Identify your most critical data. When you understand which data is most critical to your business, you can eliminate clutter and redundancies while providing stakeholders with the information they need most. 
  2. Establish and maintain data governance. A strong data governance strategy provides the standards, routines, and accountability needed to maintain data quality.
  3. Implement a centralized data model. No matter how many new systems are implemented, maintaining a single source of truth is essential to ensuring high-quality data. 
  4. Create a data-driven culture. Everyone across the organization, from leadership to frontline employees, should understand the value of data and have the capacity to use it in their day-to-day work. 
  5. Know the audience. Who uses your data, and how? Regulators? Executives? Partners? When you understand how your data is used, you can manage information and share insights more effectively.

Break Down Data Silos to Access Better Business Intelligence

When you effectively break down data silos, you can use your time and resources more efficiently to identify business opportunities and make internal improvements to your culture. Employees are more engaged when they can work on forward-thinking, exciting projects that deliver high ROI. Not to mention, you can prevent the knowledge loss that occurs when “the analytics guy” leaves the company. Getting to that point doesn’t happen overnight. At Kenway, we help clients make incremental but significant progress towards removing data silos. 

One of our clients, a financial institution, was challenged with the inefficiencies of disjointed data. The sales team struggled to understand the true breadth of their client relationships, and often performed manual tasks to piece together the client journey. Kenway performed an assessment to understand their current state, created a white paper to communicate the steps needed to realize its vision, and established a scalable data model. The financial institution now has enriched insights into its current and prospective clients and is better equipped to prevent data silos from cropping up as it grows.

Are you ready to tackle data silos at your company once and for all? We’re ready to help. Get in touch with a Kenway consultant today. 

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