The Comprehensive Handbook:
Breaking Down Data Silos
& Transforming Business Intelligence

Failing to solve these challenges can impair even the most data-driven companies. For example, different people can come up with significantly different results without a single source of truth. Managers might come to meetings with completely different insights gleaned from multiple data sources or variants of the same data. In some cases, insights and strategies may conflict.

Even if the data is accurate, it may not be complete. This can lead to making poor business decisions. In the end, users may lose trust in the data, rendering a company’s BI transformation efforts unsuccessful.

Here are some of the most common questions organizations ask when trying to overcome the
challenges associated with breaking down silos and transforming business intelligence:

Outdated Processes

Enterprises invested significantly in older data transformation processes such as Extract, Transform, and Load (ETL). This approach is based on processing ‘batches’ of data (usually in the off hours) rather than real-time information processing. Batch processing will move the data to where it’s needed, but the number of data pipelines created with this method becomes cumbersome to manage and maintain and can delay access to the data that users need.

New Data Silos

In addition to existing data silos, businesses often need to create new data extracts in response to operational demands or as a quick way to address new analytics and reporting requirements. As a result, they may introduce new software and data pipelines that add to the problem.

No Single Source of Truth

Over time these legacy systems become pervasive across an organization, adding to the complexity of managing the technical infrastructure of the business and resulting in deploying even more reporting and analytics solutions to meet the needs of the business users. Many legacy BI platforms only have access to certain types of data sources, preventing analysts from having the complete picture of the information they need. To compensate for this lack of access, analysts will create new data silos, further exacerbating the root problem. Ultimately the situation becomes unmanageable, leading to inconsistencies in the data, which eventually undermines the trust in the results generated by the reporting and analytics applications.

Lack of Consistent Data Governance

Because of the pressure on IT to provide self-service access to information by the business, data is often created for specific or immediate needs without consideration for the appropriate data modeling practices required to create a holistic and accurate data analytics platform. Disparate data integration and business rules proliferate, leading to serious data governance issues.

Relying on Spreadsheets

Even with reliable data sources and strong governance, many companies still need to rely on spreadsheets rather than properly using their data assets directly and correctly. This only adds to the number of data sources quickly becoming outdated or out of sync.

There are multiple approaches to solving these challenges, and it is important to consider them all to identify the best-of-fit solution for your organization.

Remember, a best-of-fit solution is the one that meets your people, process, and
technology where you are and melds neatly into your business.

Solve the Challenges In-House

Leveraging your in-house subject matter experts is often the first consideration for most businesses. And any solution should leverage this tribal knowledge to provide the best solution. However, your in-house expertise will not necessarily extend to the latest and most effective technologies available, and you will still need external experts in these areas. Additionally, tying up employees to create a holistic, enterprise-level solution will also introduce risk and create additional problems. Employees don’t have the time to find a solution and implement it themselves without impacting their daily operational responsibilities. Even experienced IT
teams may not have the breadth of experience — or availability — needed to implement such an enterprise solution.

Rely on Software Vendors or Cloud Service Providers

While it would be great to pull a product off the shelf and deploy it, it doesn’t work that way. Every company has different data sources, systems, and processes that require custom solutions. Software vendors will approach businesses to buy more software to fix the issues, but they are locked into their product offerings and rarely take a holistic best-of-fit approach. Cloud service providers (CSPs) will likely be part of any digital transformation, but each CSP has its pros and cons. Different providers work better and more efficiently at different aspects of data storage, data virtualization, data processing, and data democratization. The best solution typically does not lie with any one CSP.

Use Consulting Companies

Consulting companies may help create a strategy but may not implement it. Others will implement technology but do not drive a strategy to custom-fit a solution to your business needs. They may rely on best-of-breed technology instead of a best-of-fit approach.

With best-of-fit, the consulting company takes responsibility for the strategy and builds it
to fit the needs of your business’s people, processes, and technology.

What Do Industry Experts Say?

Industry experts will tell you that any of the solutions will have advantages and disadvantages but are all viable depending on your pain threshold and budgetary constraints.

There’s an old saying that applies here; there are three critical aspects to any project: speed, quality, and low cost. You can have any two, but you can’t have all three. Delivering high-quality solutions requires time and investment.

Each party that provides a solution will have its own biases. Buyers must understand any provider’s strengths, weaknesses, expertise, and bias.

Avoid any company that has a cookie-cutter solution that’s plug-and-play. Eliminating data silos and transforming business intelligence requires a considered analysis of your current infrastructure and processes to determine a custom, best-of-fit solution.

It takes careful analysis and expertise to craft the right solution for your business.

Earlier, we posed several questions that companies undergoing digital transformation are asking. In this section, we’ll provide answers along with our recommendations.

For each type of business, digital transformation will look slightly different. The objective of digital transformation is to accelerate the creation and integration of data and the delivery of actionable BI to your team.

Solutions for overcoming challenges should focus on:

• Eliminating data silos and creating a single source of truth
• Empowering all users by democratizing data
• Improving stakeholder and customer engagement
• Optimizing processes and operations
• Decreasing costs
  
  

time, this can create consensus among team members on the chosen solutions.

“Data can only take an organization so far. The real drivers are people.” - Gartner

Organizations must also put into place the processes to organize their data into a single source of truth that is accessible to everyone within the organization. This includes investing in the right self-service tools that support new technologies such as: 

• AI-driven insights
• Cloud-based data lakes
• Data virtualization
• Augmented analytics
  
  

Only then can you create a culture where data is easily accessible, insights are shared, and there is trust in the data across the organization.

1. Data Discovery includes data and API connectors to automatically discover data sources and schemas. It also includes mapping processes from the data at the source and employing pattern detection capabilities.

2. Trust in the data involves automated data quality features to automatically detect data errors, ML data quality check suggestions, the ability to monitor data, and data cleansing processes.

3. Data synchronization to standardize, match, and merge data from multiple sources into a single cleansed record and then send cleansed data back to the sources.

4. Data optimization that automates the data prep and cleansing processes for analytics and ensures that accurate data is accessible to all users within the organization.

The second step would be to audit the different management systems to determine how data is collected and how data is used within the organization. The results will help define the use cases and resources needed to implement a single source of truth. The basic building blocks of a project to eliminate data silos within a business are:

• Integrating all existing data management systems.
• Cultivating a data culture that promotes transparency, sharing, and collaboration of data across the enterprise.
• Establishing a data governance and data security plan.
• Upgrading the company’s technology infrastructure to support the new data-driven requirements.

A data warehouse is a physical integration/movement of the data that requires building an ETL pipeline of the data from potentially multiple sources into a single repository before it can be made available to audiences for analytics purposes. Solutions include data lakes, and lake houses, as part of your data fabric.

Finding the right provider to help you implement the best-of-fit solution for your business will
require due diligence on your part.

Work with a business that only focuses on data and analytics rather than a business that
sells software, does only the strategy, or only implementation. There isn’t anything out of
the box or off the shelf to fix this problem.

Data and analytics are the DNA of KASH Tech, which means this is all we do. Our expertise is focused on solving these specific issues only. We provide end-to-end holistic services and own the entire process: 

Understanding your people, process, and technology — is the path to finding the best-of-fit solution to help your organization streamline systems, trust the data, and build a data-driven business.

We are technology-agnostic. We have no agenda to sell specific software and no preconceived solution. Each client is given a unique solution to their challenges and needs.

We consider people and processes as well as technology in the solution. This also means we become part of your business team to uncover the challenges and needs. Your problems become our problems, and we define a solution around that.

We also build sustainable solutions. This means you won’t depend on other providers or us once the solution has been implemented. This is possible because KT does their consulting WITH the customer vs. OUTSIDE the customer’s team. The client has ownership in the success of the program.

Whether you work with KASH Tech or another consulting services company, here are some key things you should consider when evaluating any services provider. A quality provider should:

• Share the details of its methodology for solving the problem before you pay for the service(s). 
• Have a defined process for uncovering the challenges that cause data silos and BI issues, and willingly share it before signing up.
• Include people, processes, and technology as part of their solution. It takes a concerted effort in all three areas for success.
• Have a robust and detailed assessment process that includes an in-depth analysis of your unique systems.
• Fit your organization’s personality. You want a collaborative partnership rather than someone with a canned solution that they force onto you.
• Take a technology-agnostic approach to create a custom solution that works best for you.