Articles
How to Design and Future Proof Your Data Transformation Journey
Date: 25 Apr 2022
Despite – or perhaps because of – the pandemic disruption, 92% of companies are planning to increase spending on data/AI initiatives, according to a recent 2022 Data and AI executive survey by NewVantage Partners. This bright spot of increasing investment is no doubt laying the architectural blocks and technology foundation for an organisation’s transformation efforts.
At the same time, we see slow progress, false starts and many missed opportunities as companies falter in their quest to become data-driven with a focus on driving growth, efficiency and innovation. Far too many organisations still struggle to understand and implement the real essence of a data transformation programme. Many fail to capture the real purpose of data transformation. Too many lack the commitment to execute their strategy at a deeper level.
For some organisations that have dived head-first into implementing data analytics without aligning on data strategy, such initiatives have been bogged down by data quality issues, inconsistent or manual processes and poor ROI outcomes.
By contrast, leading organisations are implementing advanced analytics solutions to optimise their product viability, implement targeted cost reduction, improve their backend processes or create integrated front-line activities. They are progressing beyond descriptive analytics and reporting to inform decision-making. Now, these institutions are harnessing the power of advanced analytics to transform and enhance performance management across their operations from both offensive and defensive standpoints.
Take a recent Trellisi project with one client, a franchised retailer and services provider, that has long struggled with simplifying its franchisee onboarding journey. The onboarding process was managed by multiple, fragmented teams operating disparate technology platforms and processes. With no clear visibility over the end-to-end process, a patch work of manual data entry, reconciliation and inaccurate reporting impacted on productivity and objectivity.
Creating a streamlined data architecture along with data governance and team realignment enabled the company to achieve efficiencies of 20 to 25 percent in cost and productivity.
This example makes it clear that data transformation has the potential to transform a company’s bottom line. It can also improve operational efficiency and deliver problem-solving capabilities. Little wonder, then, that many organisations are keen to harness these enormous benefits.
The idea of large data warehouses with structured, source-specific data is taking a backseat as the implementation of data lakes with consolidated data in a single location makes accessibility, data refinement and analytics deployment faster and cheaper. At the same time, the increasing pace of cloud technology migration and deployment is playing a pivotal role in more effective agile development and lean innovation.
Those organisations which understand the idea of data transformation more intimately will typically establish a systemic approach. In these organisations, with successful data transformation projects behind them, data transformation has morphed from a project footing to being an equally important organisational department of its own – with the goal of integrating data-driven action throughout the organisation.
But, as data becomes more ubiquitous, thanks to the falling costs of storage and processing, companies face new challenges. For example, the need for better data governance, accessible use case models, effective monitoring and the capability to develop or recruit technical talent and data-savvy managers and executives increases.
To succeed, we advocate the systematic approach to data transformation outlined in the four-step process below.
1. Plan and Align Efforts with a Clear Data Strategy
Data transformation is best achieved with a clear goal and use case in mind. A good starting point for defining and aligning on strategy is to assess current data maturity level against aspirations and ambitions. Thinking big and setting ambitions high but staying grounded to a well-defined set of priorities will assure of a clear blueprint that is cost effective, incremental, and sustainable.
Companies have been focusing on data and its rationalisation for the last decade. With increasingly fragmented technologies, unscalable data architecture, orphaned applications and legacy systems dotting the IT landscape of many organisations, there has never been a greater need to have a highly focused data strategy. Amazingly, many institutions engaged in a transformation program lack a clear strategy.`
When unsure about what to expect from the transformation, the benefits achieved by other companies can help inspire ambition. For example:
- Newly discovered revenues
- Improved alignment with regulations
- Better compatibility between applications
- Significantly improved data operations management and governance
- Cost savings from redundant processes or duplication of resources
How do these achievements dovetail with your overall company strategy? How could data transformation play a part in achieving your strategic organisational goals? These answers are the vital first step in any data transformation project – and must be kept in mind throughout.
As with all strategic objectives, your data transformation strategy must be a) SMART – specific, measurable, achievable, realistic and time-bound, and b) laid out in the context of your organisational strategy and objectives.
Bear in mind, also, that no data transformation can succeed without the backing of the leadership team and the engagement of the whole organisation. Frequently, this can require a significant change to culture and leadership. Any preparatory programmes to establish and embed the right culture for the project to succeed need to be tackled upfront, ideally with the support of a leadership sponsor
Sidebar Highlight:
A clear and focussed strategy provides the critical framework for identifying, prioritising and executing on new opportunities to create and unlock value from your data. This effort calls for disciplined focus and targeted investment over the short and longer term.
2. Activate your Strategy through Use Cases with Quick Wins
The next step is to convert your strategic goals into lean and tactical projects. Although organisations tend to have different methods for assessing project selection and feasibility, our suggested approach is to purse moderate to high-impact use cases that can rapidly deliver results.
The use case evaluation process often focuses on three main decision pillars
- Customer impact and business value,
- Complexity and risks,
- Cost
A useful way of avoiding ambiguity is to set out clear and tactical use cases. This will help every department and employee to engage with the bigger picture more easily.
To do this, the first step is to break down the overall strategy into smaller, department-specific, or team-specific goals. You can run workshop sessions with different business units or teams to understand the key opportunities that could deliver value to each group.
Of course, each use case should align with your overall corporate strategy and remain within the limits of the organisation’s internal capacities and risk tolerance. Keep them SMART!
Once identified, the use cases can be prioritised and the most promising ones run as pilot initiatives. At this stage, communication is key. Work must be closely monitored to assess viability and performance. At the conclusion of the pilot programme, learning should be consolidated. Then the top-performing, highest-impact use cases can be rolled out for wider implementation throughout the organisation or used as a model for future use cases.
Engage and empower your teams to identify and qualify potential use cases, supported by a framework to guide this process but keep in mind that high-value projects can often come with high risks and complexity.
3. Adapt Tech Stack and Data Architecture for Agile Delivery
Any new data transformation project must be built on the appropriate data architecture and technology foundation. The architecture framework should accommodate the unique needs of each organisation’s goals and strategy. It also needs to accommodate practical priorities, e.g. legal, technical, geographical or compliance-related concerns.
Many use case initiatives require new tech tools and solutions to efficiently manage and use data across its lifecycle – from capture, store, analyse, archive and destroy – whilst serving the needs of all users.
For some use cases, a remodelled data architecture which can offer a 360-degree view of relationships with customers might be required. For others, a streamlined, frictionless flow of data between suppliers and retailers with the help of APIs might be needed. Or perhaps a data lake might need to be built to store consolidated, user-accessible data from multiple sources quickly and inexpensively.
Each infrastructure comes with its own challenges. For example, when dealing with large amounts of consolidated, unrefined or categorized data in a single location, data lakes can often become an overwhelming “data swamp”. Taking on too much raw data can prove costly and has implications in terms of security, governance and compliance as well as transfer and hosting costs.
Think critically and strategically about what data you really need to store, which can be consolidated, and which can be deleted. These considerations will play a critical role in the overall success of your project and the value it can deliver. Support from an expert partner can be of great value in this planning phase in order to prevent problems later.
The technology market for data services and infrastructure is highly innovative and fast-changing with a flux of competing solutions and applications. Finding the right expert to help select the right tech stack and provide delivery support is crucial for success.
4. Set Up Robust Operating Model for Governance and Sustained Improvements
Sustaining the transformation momentum requires a refined operating model that is focussed on embedding new ways of thinking not just about data management and governance, but also monitoring performance and optimising value generated over time.
It may be that your organisation needs to adapt its internal capabilities and team structures before it can successfully implement a transformation. The most successful data transformation projects are run by cross-functional teams with data management experts including data stewards, data quality and platform engineers, data architects and compliance and governance experts.
An experienced team like this can help organisations ensure data quality and precision and to avoid errors that may come back to bite later.
We advocate working in agile sprints as this can help speed up planning and implementation and boost success. By delivering returns and benefits sooner, it builds trust and engagement in the programme. Incremental improvements and enhancements can be delivered as the project progresses. Throughout the agile sprints, the team must ensure that common standards and policies align with the larger goals and vision of the company.
For those organisations that prefer their data-driven improvement projects to be business-led, we recommend setting up a centre of excellence with responsibility for setting standards, promoting best practice, educating project teams and business groups and monitoring performance and compliance. The centre of excellence might also take on a strategic role in prioritising and greenlighting those projects most closely aligned with delivery of corporate strategic goals.
Many smart organisations outsource their data management and governance responsibilities to a specialist third-party. Other organisations operate their data program as a complete, separate business unit with strict protocols for controlling data quality and maintaining accurate records of data input.
Either of these three models can work; how you decide to manage data governance is less important than embedding compliant practice throughout the organisational data architectures, systems, processes and users. Don’t overlook the importance of education and communication in safeguarding compliance objectives – whether GDPR, legal, financial or business. Everyone in organisation must understand their role in ensuring good data governance, data integrity, secure information management and compliance.
Like any change management effort, data transformation is not a one-time project with a defined end – it requires a continuous cycle of change and improvement with dedicated expert support every step of the way through.
How Trellisi Designs Your Data Transformation Journey
Tresilli’s team of data, project management and technical experts can provide assistance at all and any stages of your data transformation journey, so you can draw on our expertise as and when you need it.
We can support you as you prepare a clear data strategy and then convert the data strategy into practical use cases. We can help you create or adapt your data architecture to the needs of your use cases. We can also advise and assist as you develop and embed a robust data governance to ensure data quality. And we can help you create and run agile teams for project management and delivery. Throughout, you draw on our expert team’s experience to boost the success of your data transformation project.
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