On-premises

Multi-cloud – Distributed/federated

Hybrid cloud strategy – On-premises and cloud but distributed

Primary cloud strategy with a focus on multiple consumptions capabilities

Manual framework for data intake and processing

Disparate ETL tools along with manual transformation processes

Multiple tools with a common framework for data delivery

A single enterprise-standard data integration platform

There is no sensitive data

Only customer data is considered sensitive, and the rest follows standard data privacy policies

Dictated by information security and company policies

Strict adherence to regulations and compliance

Data is not saved in an organized or standardized manner

Data is mainly kept in spreadsheets on local computers or in siloed databases across the organization

Data is stored in a central data warehouse

Data is stored in a data lake, where it can be utilized extensively across the organization

More than 50%

20 to 50%

10 to 20%

Less than 10%

Too many changes in the last one year

Significant changes before the last six months but few changes now

Few changes in the past six months but many before that

The landscape is stable and has not undergone significant changes recently

No unstructured or third-party data involved

Yes, periodic data availability based on requests

Yes, near real-time availability of data sets

Yes, all data sets are available in real-time

Each line of business owns its own data, but it’s completely ungoverned

Managed manually with a higher lead time

Federated, but completely automated

A single version of truth for all businesses

I never get the information when I need it most

There is a process of getting urgent information, but it is too cumbersome and bureaucratic

The infrastructure enables me to get it, but it’s quite complex and requires technical knowledge

We have a fully integrated cloud platform for on-demand reporting needs

More than 40% of the information

15 to 40% of the information

5 to 15% of the information

Less than 5% of the information

Poor perception with no usage of data for meaningful decisions

Average perception with limited use of data for daily business needs

Mixed notion where data is validated before taking decisions through data quality checks

Perceived highly with all decisions based on information available in data lakes/data warehouses

Users create their own set of applications with data directly extracted from the source

There are too many systems, and it adds to the confusion; there is no endorsed set of metrics

The IT team acquires information and subsequently shares it with the business stakeholders

Self-service approach – the right information is made available to all stakeholders in real-time

The data operations team reviews reporting needs based on ad hoc requests from application users, and key business stakeholders validate it

Each business unit defines its needs at the beginning of the year

Design thinking workshops conducted with key business personas

Periodic digital assessments

Non-existent analytical capabilities

Access to standard reports but limited analytical capabilities

Reporting and analytical capabilities of specific user profiles

Full-fledged reporting and analytical capabilities

No automation is in place; we manage all reporting tasks manually

We sporadically use automation using legacy tools, such as Excel and VBA

We have automated core reports using BI platforms

We automate all reports using a combination of AI and ML

There is no enterprise-wide data quality strategy

Data quality is not an explicit focus area but whenever a data error is reported, we investigate the issue

Data quality monitoring happens as data moves to data lakes/data warehouses

Data quality monitoring happens at the source itself

We do not maintain metadata regularly

Updates are periodic as stated by the CDO or the data office

Updates are event-/need-based

We update it in real-time

We do not follow any data governance model

Limited data governance capabilities with focus only on data privacy

A dedicated data governance body owns all critical data elements

Lean and automated data governance aligned to industry standards

Less than 75%

75% to 90%

90 to 95%

More than 95%

There are no agreed or published standards

Documentation exists around analytical processes, and we recognize the need for standards

Guidelines are documented thoroughly, and we have enforced standards across the organization

Advanced processes involving extensive use of AI are in place to enforce best practices for insights