The art of making data work

Published on: April 1, 2019

Data is the lifeblood of organizations. It is constantly being created, meaning is attached to it and decisions are based on it. But in order to make data really work, organizations need to know how it is created and archived, who is responsible for data, and where data is used in business and its processes. This is the playing field of data management, a discipline that is gaining more and more importance.

IT projects have traditionally been a matter of delivering value through functionality. IT has always been about tools that allow people to work more efficiently or that provide better customer experiences. Databases, servers, infrastructure, backups and migration are just some of the typical elements that need to provide that required functionality.

But organizations have gradually come to realize that functionality is nothing without quality data. Today, data management is high on the agenda of many modern organizations. Entering data is no longer an afterthought. Instead, data management becomes more and more embedded in the daily operations.
Kapernikov helps companies to make data work in their daily operations.

In order to enhance data quality, we work towards a Single Version of the Truth. This means that we design processes and their underlying architecture in such a way that each piece of data is stored in a single system. As a result, everyone works with the same data and knows its original source.

Data challenges

Data errors and data gaps limit the usefulness of an application. Garbage in, garbage out. When data is inaccurate, users will no longer trust the application, and they will no longer be motivated to use it.

When many applications rely on the same data, and data management is not coordinated properly, the inconsistencies of unsynchronized databases may pile up. As a result, data exchange between applications becomes problematic and operations are hampered.

Data management for utility companies

Utility companies have a lot to gain with data management, because there, a correct overview of assets is business-critical.

In a regulated environment, the tariffs (and therefore income) depend on an accurate asset inventory.
For the safety of operations, staff and stakeholders need an up-to-date overview of all installations.
Data also includes the definition of different roles: who owns the asset, who is responsible for maintenance, who is operating?
Reliable data about assets makes it possible to develop smarter policies for maintenance, replacement and investment.

Data management for grid operators

Data management is definitely a challenge for grid operators. These companies manage infrastructure that is interconnected and often spread out geographically. Building and keeping inventory is labor-intensive.

Kapernikov maximizes the recuperation of existing asset data and has a proven approach for in-the-field inventory management, using existing teams or providing specialized, temporary workforce.

Also mapping connections between assets is a challenge. Assets in tightly interconnected infrastructure cannot be managed separately, as if there would be no mutual impact. Kapernikov establishes the links between objects and valorizes them, to ensure consistent data across all assets in the grid, including their interconnectivity.

Three steps towards optimal data management

Ensuring data quality in processes

We chart and model all activities of the organization where data is manipulated, both by users and by machines, in business and technical processes. Wherever data is involved, we incorporate fault tolerance. We often introduce extra manipulations to close loopholes in processes without introducing bureaucracy or making operations difficult, and we incorporate control mechanisms. This way, data remains reliable over time.

Improving access and data architecture

We design or redesign data flows, databases and data schemes, and provide a common language for users. Consistency between databases allows users to consult and report across applications. High-quality consistent data and an efficient data architecture allow for analysis in real-time. These are also prerequisites for many new “maintenance 4.0” applications, such as condition-based maintenance.

Efficiency and simplification

Data entry and manipulation is labor-intensive. It is easy to make mistakes and dedicated technical workers usually find this task unpleasant. That is why we aim to have information entered only once in a digital way. All data manipulations by users, but also by machines, have to be revised for efficiency and to avoid additional manual operations. Automation and machine-assistance are key. Technologies borrowed from A.I. help us a long way to facilitate data entry.

Principles of successful data management

1. Visibility for management and data managers

The consequences of missing or inaccurate data may only be felt after months or even years. That is why management and data managers need controls and KPIs in a daily status overview. This allows them to discuss data quality in an efficient way and to identify where improvement is needed.

2. Uniform naming

Objects should be named consistently to ensure quality, uniqueness and personal and operational safety. During a data alignment project, we completely eliminate historical inconsistencies and create naming guidelines.

3. Clear definitions

Every sector has its own jargon. Even within an organization, a single term can have multiple meanings to different departments. During a project, we ensure that everyone uses the same language and we provide a glossary or information model.

4. Clear rules for modeling reality

Uniform coding is essential to ensure human comprehension and automation. We ensure consistency with a practical fill-in guide for data managers.

Let’s make data work for you

Does your organization need correct and accurate data, consistent across databases, and delivered in real time? We can help. Let us know what your challenges are and maybe we can make data work for you.