According to a McKinsey report— one US bank expects to see more than $400 million in savings from rationalizing its IT data assets and $2 billion in gains from additional revenues, lower capital requirements, and operational efficiencies. Data is the key to digital transformation of enterprises. Data transformation for enterprises surpasses the basics of collecting, preparing, storing, and managing data. Data transformation for your organization is about the ability to manage enterprise data more effectively to extract value from those data. This is also frequently called “being data-driven.”
Digitally transformed organizations are implicitly data-driven and to become one implies the following changes:
- Make data accessible (with the right quality, security, etc.)
- Make sure that individuals have the tools and the skills to use data to derive insights and support decisions.
- Transform business processes to effectively incorporate data-driven insights.
- Shift from a gut-feeling-insights mindset to a based-on-data-insights mindset.
This article explores some of the key practices that can help your organization to implement data transformation.
How to Implement Data Transformation in six Simple Steps
1. Define a clear data strategy
Start by setting clear goals about the values you want to create from data. To define your data strategy map and understand your current data landscape, your technology stack, and the internal skills and capabilities your people possess. Identify your goals at organizational and individual business levels and break down your data strategy as per these goals. Clearly outline the importance of data to your business for stakeholders’ buy-in. You may consider the appointment of a Chief Data Officer at this stage who can define and lead your organization’s data transformation strategy. Draw up a shortlist of use cases with the greatest potential for impact, ensure they are aligned with broader corporate strategy, and assess their feasibility in terms of commercial, risk, operational efficiency, and financial control.
2. Inculcate a data culture within your organization
According to a McKinsey report, digital transformation project where at least 7% of employees felt involved doubled their chances of better ROI. Data transformation in your enterprise calls for employees to become fluent in utilizing data related tools and leveraging data to improve their workflows. The challenge is a majority of them are not data specialists and lack confidence in utilizing data in their work. It won’t matter if the CEO launches an amazing new self-service data portal without making your employees see the value in it. We must educate employees at all levels of the company to understand how it helps them do their job better and faster. Also, we must demonstrate what data is available and take feedback from them to provide datasets that help them with their job. Nurturing a data transformation culture in your organization demands time to train people on the basics to build their confidence and ensure they feel comfortable using data on a day-to-day basis. You may consider recognizing data champions from around the business to shepherd and encourage staff involvement.
3. Ensure robust governance across enterprise
Data transformation programs are frequently extensive and require full organizational involvement. To ensure their efficacy, they must be handled carefully and have robust governance procedures in place. Governance can be bracketed into three major silos:
Data governance – it is the process of making sure that all data, regardless of where it is generated or how it is utilized, is consistent, well-documented, secure, compliant, and adheres to company requirements. This may involve creating a data dictionary and a full list of metadata. This also involves determining the ingestion strategy like ETL or ELT, or the storage infrastructure as data lakes or data warehouses.
Technology governance – is the process of implementing the appropriate technological solutions with an agile infrastructure that spans the whole data lifecycle, from collection to sharing and repurposing, to support your data transformation strategy.
Project governance – effective management and governance framework for the entire data transformation effort. This is especially crucial when developing use cases and pilot projects for the entire company. These need to be documented, monitored and evaluated to ensure that they are benefiting the business.
4. Adopt a supporting data infrastructure
Leading organizations are transforming their data architecture to meet diverse business needs and unlock business values around data. Many are adopting data lakes for ingestion of streaming data in real time, but you will still need your data warehouses for batch processing tasks like financial report generation. Legacy and new storage systems should be able to coexist in your data infrastructure as migrating all data might not be feasible or cost-effective. Also, enterprises rely on a lot of third-party data sources which must be bridged via API. Cloud platforms offer a vast range of solutions such as PaaS (Platform as a Service), IaaS (infrastructure as a service) for data storage and management and computing services like GPUs for model training and analytics. You may hire a managed service provider to outsource your IT infrastructure management to reduce cost or engage your capital and resources for higher-value initiatives.
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5. Improve data accessibility to implement data democratization
Traditionally data exists in departmental silo making it inaccessible enterprise-wide. Data accessibility sets your enterprise on the path of data transformation by making available data suitable for use, regardless of how much experience or expertise a particular employee has. It establishes data democratization- every person in the company has unequivocal access to the data, and there are no protocols that can cause hurdles or block their path to data access. However, keep in mind although it is very important to make data accessible, it is even more crucial that the data is secure, and its integrity is maintained. Your enterprise data manager should design policies and protocols that can be implemented in order to maintain integrity and access to the data.
6. Make data understandable and reusable by everyone
Majority of employees come from a non-technical background or lack the skills to use BI tools and analyze data for their benefits. Business should bring context to raw data and make it meaningful to every employee. They should implement data visualization techniques via dashboards, maps and graphics. This sparks interest in the employees and encourages reutilization of data. Ensure that it is easy to create visualizations and that they are available in the right formats to match potential uses. Visualization tools should be intuitive, and outputs (reports, graphs, charts) should be simple for everyone to understand and share across the organization.
Conclusion
Data transformation of your enterprise will become successful when you have a defined data strategy, supporting infrastructure, and structured governance implemented. The above six steps can serve as a guideline for your enterprise to become completely data-driven and meet the maximum ROI for digital transformation investments.
