What is Data Maturity & How to Measure your Organisation’s Data Maturity Level?

Data science

What is Data Maturity & How to Measure your Organisation’s Data Maturity Level?

Big Data is the new buzzword in the modern technology landscape. But, it is not a
miraculous process. It is a combination of enormous data streams and setups arranged
analytically for better decision making. If the data isn’t properly arranged, then big data isn’t
anything more than a bunch of enormous data lying around. Thus, a massive technology to
extract the data from different business sources to deliver self-driven analytics with the
reduced time and cost is required – Big Data Management Maturity.

What is a Data Maturity Model?
Data maturity is the extent to which data is used by an organization. The more data is used
by an organization, the more it will grow in its data maturity level. As data matures the
organization too gets higher on the maturity scale. The scales or stages in which the data
maturity level is analyzed is called the Big Data Maturity Model.

It indicates that an organization using the business intelligence and high tech analytical
software can analyze the maturity level of their organization’s through the data presented
on the spreadsheet figures. The data maturity journey of an organization is a scaling

What is the purpose of Data Maturity?
The “maturity” can be referred to in different contexts, but in this post, it is an analytical
maturity curve to measure the maturity of the organization to undertake continuous
improvement in a particular discipline. For continuous improvement companies need to
assess data maturity checks –
● Check the ‘as is’ status
● Check and specify ‘to be’ situation
● Remove the gap between ‘as is’ and ‘to be’
● Mark the definite results

By developing a metadata maturity model, an organization can set up a foundation for the
basic strategies, plans, and actions to achieve the ‘to be’ situation. The benefits of using
data maturity assessment are pretty common. All the companies manage data in a
particular manner. So, practically every company has a systematic model to measure its
mature data.
The analytics maturity process is a continuous process that assists companies to
continuously measure their performance and progress to achieve the marked results. There
are plenty of different stages which companies need to complete to measure the maturity
level of their data.

How to Measure Your Organization’s Data Maturity?
To measure the data maturity level of an organization, a systematic data quality maturity
is required that can measure data maturity at every level. The stages of data maturity
measuring are –

Awareness Stage
Normally, organizations manually compile reports from various external and internal
sources to create a standard report. All the different departments working in the
organization such as sales, finance, and production departments are creating their individual
reports. When all the departments are making their own reports, then this lacks uniformity,
thus the overall performance of the organization can’t be predicted.
The presence of different business intelligence systems, data sources, and databases
without proper integration can create havoc in business reporting. So, if your organization
has multiple reporting systems, then you have to focus on scaling up the capabilities of your
organization to move up on the maturity curve. The different data maturity measurement
levels need to be standardized such as – data modeling, designing, normalization, and so on.

Quality Check Stage
It is the stage of data maturity measurement where the data quality maturity model is
established to question the quality of the data. IT departments have multiple databases and
incomplete data houses without any integration. This unfiltered raw data has no purpose or
quality. Thus, the businesses started to track down organizational KPIs and data sources to
find a way to use unstructured data. It is the stage of finding innovative data management
solutions beyond the capacities of the IT.
Companies should divert their attention to building proficiency in data quality and
integration for speedy access. The advanced technologies can help businesses in managing
the unstructured data and making the roadmap of data management strategies These
actions will also ignite the standard reporting system across the firm. With the data quality
check, organizations can effectively use their resources and understand the performance of
big data and analytics in daily operations.

Weaponise Data Stage
This is the most interesting stage where firms are using the data as their critical business
weapon to make important decisions. It is the incredible stage where marriage between IT
streams and business goes to the next stage of the execution. All the resources are placed in
the right position to break down both data and organizational silos. The different business
functions are using competitive differentiators.

IT needs to constantly implement the new technologies to integrate data sources and
applications so that data can effectively be served on the demand. The IT and business
amalgamation should focus on designing advanced sources such as text timing, data lake,
data mining, predictive analysis, and so on.

Data-Driven Stage
The final data maturity stage is a state of bliss. It is the stage where data is so deeply
integrated that all your decisions become extremely data-driven. The objective is to scale
the data strategy while continuing to take out costs. At this stage, IT and business are
working as two tight knitted friends to form a cohesive unit. By this phase, IT has integrated
all the data sources and implemented an advanced analytical funnel. On the other hand, the
business has recognized all the places where they can embed analytics.
The thing that remains in the data-driven stage is the realization of a competitive advantage
in the maturity model. The competitive analytics turn the maturity curve from descriptive
and predictive analytics into prescriptive analytics.

Where Do You Stand In Data & Analytics Maturity Level?
The general purpose of the data analytics maturity model is to develop a comprehensive
framework to make it easier to examine the most vital parts while setting up a solid data
analytics foundation. So, if you want to know where your organization is standing in the
data and analytics maturity level, then you have to measure your organization on the
following key factors.

  1. Strategy
    The motive of gathering the data and goals need to be defined first. The strategy helps in
    marketing and managing your business towards the data-driven culture. Thus, the
    important factor to determine the strength of the data governance maturity model would
    be clearly defining and communicating the strategy of the organization related to data.
  2. Capacity
    Another parameter to assess the level of data maturity of your organization would be
    analyzing the resources and skill sets for data maturity. The expertise of the relevant
    stakeholders plays a vital role in data and analytics maturity. By analyzing the organization’s
    operational models, you can evaluate the capacity of data maturity.
  3. Technology
    Today, marketing highly depends upon the tools. The functionality of the tools can change
    the data maturity level. It is important to have adequate technical resources to properly
    analyze the data and analytics maturity level. Thus, if your organization doesn’t have highly
    functional tools and technologies, then you will stay behind in the data maturity
    measurement cycle.
  4. Process
    To know how data-driven decisions will be adapted in the culture, the process needs to be
    elaborated. The process will show how data analytics services go beyond the spreadsheet
    and how to maintain data quality.
  5. Insight
    The final step to assess your position on the data and analytics model would be evaluating
    how data can be turned into meaningful information to generate the relevant insights. By
    forming all the five steps, you can arrive at where your organization stands on the data
    maturity ground.
    Why Data Maturity Is Critical?
    Today, all business organizations have their own data management system. But, the
    relevancy and results of the unstructured data management models are zero. Thus, if you
    want to gain benefit from your vast business data, then establishing a proper flow of data
    maturity is highly important. It is the simplest way to empower your business through the
    relevant data.