3 Challenges in Data Governance

1
3 Challenges in Data Governance

• Contoural Inc

2

• Data governance is the process of managing and
  handling data for efficiency, integrity,
  usability and security of the data. Mainly used
  in corporate setups, data governance is
  implemented in compliance with data policies and
  standards to have good control on data usage.
  This article discusses 3 main challenges faced
  during data governance and the need for data
  governance consulting.
• The main idea behind implementing data governance
  is to ensure data is consistent, reliable and
  secure. However, because of data privacy
  regulations, organizations face new challenges.
  Therefore, in order to optimize operations and
  make business decisions, they have started
  relying mainly on data analytics.

3
Data Governance Program

• An efficient data governance program normally
  comprises of a data governance experts, a
  governing body which devises standards and
  policies, and data stewards to enforce
  procedures. Of course, the program cannot run be
  implemented efficiently without inclusion of IT
  team, data management team, executives and
  representatives from other operations of the
  organization.
• Due to the complexity of data structure, data
  governance consulting is of critical importance.
  Lets take a look at 3 biggest challenges in data
  governance which call for the need of data
  governance consulting.

4
Project Data Governance Challenges

• The beginning of data governance is challenging
  and complicated because there are various parties
  involved. Due to involvement of many
  stakeholders, there can be conflicting and
  diverging views of key enterprise data entities,
  such as customers or products, which have to be
  brought on the same page. These conflicts must be
  resolved right in the beginning, as part of the
  data governance program, so that there are no
  resources wasted. Sharing the same vision and
  approach can be a stressful undertaking, which is
  why the data governance consulting needs a clear
  dispute-resolution procedure, right in the early
  stages of program.
• Below, we have discussed 3 major challenges which
  organizations face regarding data governance.

5
Demonstrate its Business Value

• Getting all conflicts resolved in the initial
  phase can be really difficult, but you can tweak
  the whole program with the help of attaching
  business value and goals achievement. By
  attaching the expected outcomes of the program
  with specific corporate priorities, you can bring
  various representatives on the same page.
• In order to show business value, you need to
  develop measurable metrics, especially regarding
  data quality improvements. Demonstrate the number
  of data errors resolved at regular intervals, and
  how they reflected on revenue gains. You can also
  show how resolving data error lead to saving you
  cost and time of dealing with a major business
  blunder.
• Other than this, you can also show how you can
  measure the accuracy and integrity of data, and
  its completeness with the help of data quality
  metrics. You can do more research about more
  metrics of data which impact data governance and
  quality, and have influence on business outcomes.
  This will bring more credibility to a devised
  data governance program, and help in its
  implementation without any conflict.

6
Supporting Self-Service Analytics

• Because of the self-service BI and analytics
  movement, data governance is facing new
  challenges because of giving data access to more
  users in organizations. Data Governance programs
  must ensure the accuracy, accessibility and
  usability of data to the authorized people for
  self-service users. An effective data governance
  system also ensures that the data users such as
  business analysts, executives and citizen data
  scientists, don't misuse data, compromise data
  privacy, and violate security restrictions. The
  process gets more complicated for streaming data
  that's used for real-time analytics.

7
Governing Big Data

• Huge data systems have huge maintenance
  challenges and needs. Previously data governance
  programs were only focused on structured data
  stored in relational databases. However, now they
  deal with structured, unstructured and
  semi-structured data that big data environments
  typically contain. Also, they rely on a variety
  of data platforms such as Hadoop and Spark
  systems, NoSQL databases and cloud object stores.
  Because of enormous amount of data, now the data
  is stored in raw form in data lakes and is
  filtered as needed for analytics uses.

8
THANK YOU