We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive.
In today’s day and age, data can be found just about anywhere. The volume and types of data are growing by the day, making data processing and generation of data insights more and more complicated. According to Experian’s 2019 Global Data Benchmark Report, companies believe that 29% of their customer and prospect data is inaccurate in some way.
Inaccurate data leads to generating unreliable insights which, in the long run, lead the business in the wrong direction. This is why dealing with data should be your top priority if you want your company to digitally transform in a meaningful way, truly become data-driven, and find ways to monetize its data. The key to laying the groundwork for achieving this? Employing Enterprise Data Management (EDM).
What is enterprise data management?
Sisense’s State of BI and Analytics survey of 500 companies found that 34% of them were increasing their investment in analytics and BI and expanding use cases. Companies looking to do more with data and insights need an effective EDM setup in place.
Simply put, enterprise data management is the process of centralizing, standardizing, and organizing data in order to produce high-quality, accurate insights that improve a company’s decision-making and overall use of data. EDM involves a wide spectrum of activities directed toward organizing not only a company’s data but also its people.
Establishing an effective EDM system is no easy feat: this requires deep knowledge of the company’s structure and internal and external processes. The team in charge of your company’s EDM is focused on a set of processes, practices, and activities across the entire data lineage process.
What does enterprise data management do for your business?
Establishing a well-defined EDM framework primarily means defining and enforcing rules and regulations that will unify the way your business uses data in all its processes. Standardization across an enterprise is crucial for many reasons. First off, this involves defining workflows for every business process within the enterprise: the what, how, why, who, when, and where aspects of data.
EDM covers the entire organization’s data lifecycle:
- It designs and describes data pipelines for each enterprise data type: metadata, reference data, master data, transactional data, and reporting data.
- It specifies data structure definitions for each dataset being used: the user of a particular dataset will immediately have the information about the type and structure of data contained in it.
- It ensures that business rules are valid and implemented in the right places within the process of transforming data into the final product.
The main components of enterprise data management
Like any complex system, your company’s EDM system is made up of a multitude of smaller subsystems, each of which has a specific role in creating the final data products. These subsystems each play a vital part in your overall EDM program, but three that we’ll give special attention to are data governance, architecture, and warehousing.
- Data governance is the foundation of EDM and is directly related to all other subsystems. Its main purpose is to establish an enterprise data management strategy. That includes the creation of fundamental documents that define policies, procedures, roles, tasks, and responsibilities throughout the organization. These regulations, ultimately, ensure key business values: data consistency, quality, and trustworthiness.
- Data architecture creates instructions that guide you through the data collection, integration, and transformation processes, as well as data modeling. The main purpose of setting up a functional data architecture is basically structuring and securing the operational environment in which the data will be stored and manipulated. Data security is also a part of this field.
- Data Warehousing and BI represent the analytical core of an EDM system. Data warehousing reorganizes input data into a format that is most suitable for data analysts looking to create reliable insights. Business Intelligence also empowers end-users with those insights, enabling them to make smarter, data-driven business decisions.
Benefits of enterprise data management
A true value of a well-defined EDM lies in a holistic approach to data, focused on the meaning, quality, and usefulness of the data to the business.
A good EDM setup makes your organization more productive as a whole and helps you:
- Build a scalable data architecture that allows your system to adapt quickly to changes
- Ensure data consistency
- Perform accurate data analysis and produce reliable insights
- Embed insights into products easily
- Set solid foundation for data monetization
Who is in charge of enterprise data management?
Data management is an important job, but often companies think about it too late or not at all!
“Everybody is going to assume that somebody else is taking care of the data,” says Charles Holive, Sisense Managing Director of Data Monetization and Strategy Consulting. “And the result is, nobody does.”
Enterprise data managers play a major role in defining EDM standards, policies, and procedures. These experts usually have either an IT or economics background. Most commonly, enterprise data managers are either database administrators, IT administrators, business project managers, or IT project managers, who have many years of experience in the industry.
They are responsible for overseeing a company’s data systems/subsystems, as well as monitoring the archiving of data that is no longer used. In other words, they control the entire data lineage, ensuring the data is highly reliable and can be used for producing precise data insights.
How to define your enterprise data management strategy
There is no single EDM strategy that works for every organization: each company needs to define its own strategy based on its particular needs. However, there are some practices you can follow and some questions that should be answered when designing an effective EDM strategy:
1. Evaluate the current system: gather and analyze the information related to the organization’s characteristics, activities, and business targets.
– Which subsystems does the system consist of and how are they interconnected?
– What are the inputs and outputs of each subsystem?
– Where are bottlenecks that slow down data flow processes?
2. Define data products: gain a clear understanding of your end goals and what you want to achieve by using EDM in your company.
– What are the products that you want to have as final outputs of your EDM
– How do you plan to use these final data products?
3. Establish standards, policies, and procedures; implement data governance:
– Identify organization-wide activities that a company needs to implement in
order to construct a conceptual framework which will support the production of
reliable and high-quality data products
– Define regulations for each subsystem and activities within
– Decide on how to perform the execution of each identified activity
4. Define teams and their responsibilities:
– Allocate activities to particular departments/teams
– Define vertical and horizontal hierarchy across the company
– Determine individual responsibilities within a team
5. Implement a flexible data architecture system:
– Decide on which hardware and software technologies you plan to use
– Identify data structure definitions/format of data to be collected and
– Automate all data processes by creating data pipelines
– Identify how data changes in the transition process:
6. Communicate with your employees and your partners: keep all parties up-to-date when it comes to system changes:
– Educate colleagues about technological, structural, and system updates
– Notify external partners about new standards and procedures that affect them
Building a system for long-term success
Diligent maintenance of data and data processes is crucial to a company’s success. The proper implementation of an enterprise data management system helps your company make smarter, data-driven decisions and it can also make it easier to integrate the insights from your data into your products. Beyond technological considerations, it also standardizes your data and work culture, improves both your internal decision-making processes and communication with your partners, and saves you time and money by reducing the costs of dealing with low-quality data.
If you don’t already have a strong EDM system in place at your company, implementing one will not be an easy task. However, all the effort you make will pay! Today’s world is already governed by data. Every company is becoming a data company, so there’s no better time than today to take advantage of EDM and prepare your company for long-term success.
Scott Castle is the VP & GM for Cloud Data Teams at Sisense. He brings over 25 years of experience in software development and product management at leading technology companies including Adobe, Electric Cloud, and FileNet. Scott is a prolific writer and speaker on all things data, appearing at events like the Gartner Enterprise Data Conference, Data Champions, and Strata Data NYC.