Data Unification and Breaking Down Silos

We can describe a “data silo” as a group of data accessible by a particular party or parties in an organization and not easily or entirely accessible by others within the very same organization, similar to how farmers store different types of grain products.

As individual departments within organizations such as HR, Finance, and Sales usually require different sets of information to operate, these departments mostly tend to store their data in separate locations, which are usually referred to as “data silos.” With the significant growth in the amount and diversity of data assets, data silos are continuously expanding, thereby creating barriers to information sharing and collaboration across departments. In addition, the data of several departments potentially overlap and create inconsistencies and poor data quality overall. Incorrectly structured data silos can potentially cause errors in consolidated figures and prevent real-time data-informed decision-making.

A?McKinsey & Company Study?clearly indicates the negative correlation between silos and company performance and the corrosive effect. Economic performance declines as siloed mindsets and behavior increase within a company. The study also states, “Working in silos can cause tunnel vision, tribalism, and weak corporate performance.” Any business that initiates its digital transformation plan without breaking down data silos will most likely miss the opportunity to become data-driven.

In this article, we will look into some common issues and proven methods to surpass the silo effect and increase overall efficiency.

1) Review team structures and KPIs

Many businesses have fragmented and fiercely guarded ownership of processes and information, with roles designed around limited requirements, as indicated in the “Making collaboration across functions a reality” article by McKinsey & Company. This fragmented structure results in internal complexity that restricts collaboration across business units. Additionally, companies that use traditional methodologies either overburden their managers with requests to revise every process across business units or, inversely, have a narrow focus within their functions.

Real-life Example

A communications-services company discovered that their field engineers were spending very little time with customers during installation due to?installation time?KPIs, which lasted in an excessive number of help requests to call centers and decreased overall customer satisfaction.

The company resolved this problem by setting several cross-functional targets to align different teams and directing them to do more than their usual work practices and patterns. Additionally, the company created new cross-functional teams assigned to oversee the end-to-end installation process from initial orders to after-sales service. In consequence, teams that traditionally had distinct workflows and little shared responsibility had to leave their comfort zones.

This new structure has had a substantial impact on the results: “first-time-right delivery has increased to over 80 percent (from 65 percent), customer satisfaction increased, and the number of requests for help to the call center during the first six weeks after the installation has dropped by one third.“

We should note that it is critical to determine clear roles and responsibilities for team members to overcome the uncertainty after leaving their comfort zones. As?PwC Study?states: “Freed from the natural comfort zones and power structures of their silos, employees in cross-functional teams can be uncertain of priorities and expectations.” For joint teams, businesses should determine who is accountable for final sign-off and who from the business and functional units must be informed.

2) Reimagine roles and processes

Teams from different disciplines should not only align on daily and weekly tasks but also truly empathize with each others’ challenges, skill sets, and jointly owned targets.

Real-life Example

A global industrial company revealed that its internal complexity was hindering innovation efforts and mission-critical business units had severe performance issues. Each business unit had its own P&L, unique terminologies, atomized processes and approaches, which led to problems with information and skills sharing across teams, and eventually undermined the expected economies of scale.

Their solution was to redesign a tightly defined architecture with two layers: a bottom up end-to-end view of markets and customers, and a top-down operating model. (See below matrix)

The company identified various matrix-like operational units within the company known as business-market combinations. Each unit leader co-owned P&Ls and had the freedom to abolish traditional ways of working and shape any cross-functional and cross-business combinations. They also established comprehensive targets that could not be met by any individual function or business, such as becoming a market leader, reaching new emerging market segments, and adopting new business models and sales channels.

End-to-end Operating Model | Source:?McKinsey & Company

3) Own the Transformation and Appoint Leaders for Change

It may take significant work to convince others that change is necessary and to break their habits. If individuals do not like the new system, they will not quickly embrace it; transformation has an emotional side.

Management should show their commitment and persistence to this transformation by appointing a team of transformation leaders that effectively communicate the data sharing and data integrity benefits, the data quality problems, and how this harms the competitive advantages to help avert cultural resistance and have a smoother transition. Management should also avoid cross-departmental competition and clearly state their expectations regarding companywide collaboration and communication.

A way to break the silo is to create robust “process feeders” or “global process owners” who can drive horizontal integration. Alternatively, a senior leader with a mandate from the CEO, COO, or CFO can pull work out of these functions to create a stand-alone unit.

4) Introduce a Standardized Model for the Overall Organization

It is understandable that due to data regulations and confidentiality concerns, not all people can (and should) access and work with every piece of data within an organization. However, when different departments work with various tools and models, it is inevitable to have growing data silos. Adopting a standardized data model, policies, and controls for the entire business can help identify the silos.

To do this, executives should assess the different data storage and management systems each department uses, how the data is collected and used, and whether the information is relevant for other teams. It is also essential to identify the challenges of current data systems to understand the requirements of how data should be consolidated.

While the relevance and importance of data have risen in recent years, its acceptance and impact have been curtailed by several barriers to widespread adoption. In a?survey from Deloitte, 31% of the respondents stated that their data is unintegrated; data is usable, but in functional or process silos, and their senior executives do not discuss data management.

It is wise to keep in mind that while departments operate separately, they are also interdependent and exist to support a common goal. While some sales department data may be a subject for analysis by the administrative departments, some finance department data may be relevant for the internal audit team.

Another major obstacle caused by data silos is reflected in customer satisfaction levels. In today’s customer-centric market conditions, businesses should first genuinely understand their customers to sustain growth. This is only possible through centralizing customer data and having a unified customer view.

5) Adopt a Cloud System to Democratize Your Data

Organizations often leverage tools to address one use case at a time, as these tools are usually not agile enough to meet the organization’s changing needs. As new use cases emerge, the data environment changes, teams grow, and requirements change.

Many organizations are unintendedly pushed into silos due to their legacy systems. When departments use different tech solutions and tools (i.e., spreadsheets, accounting software, CRM tools) that store and manage data in different ways and formats, sharing data with stakeholders in another department or consolidating the data for a broader perspective may become very challenging.

Accelerating collaboration is more likely with the right technology. When appropriately deployed, technology streamlines a data-driven culture. Businesses need a data environment that facilitates collaboration and communication.

The most effective way to break silos is to flow all corporate data into a cloud-based central data repository (such as a data warehouse or data lake) by consolidating all data types and formats and granting different access levels to sustain privacy and security.

Bonus: Train Your Team and Ensure New Tools and Leveraged

Forrester Report?indicates that the most common challenges faced by CDOs when trying to implement a data program are resistance to change and slow adoption. Employees are often hesitant to welcome new tools and processes and find them difficult even if you deploy cutting-edge technology. The usage of the new and shiny tool and technology is only limited to its adoption by the users.

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If you want to unlock your data’s potential and drive innovation taking comfort in Kartaca’s 10+ years of experience, please check?our page?and?contact us.

Kartaca is a Google Cloud Premier Partner with approved “Cloud Migration” and “Data Analytics” specializations.

Author: Gizem Terzi Türko?lu