In the past few years companies have been spending more money on data platforms to be modern.In recent few years, companies have been spending more money on modern data platforms. The major paths followed have been listed below:
- Cloud migrations
- Real-time analytics
- AI copilots
- Self-service dashboards
- Generative AI integrations
Many businesses appear to be even more modern than ever they’ve been on paper.
But, there is always a familiar question which comes up in reviews of transformation and leadership:
So why is it that we’re not seeing the business impact that we’d thought of?
The answer is not comfortable given that it is a challenge to one of the greatest assumptions with digital transformation:
Data modernization is more of a technology problem than ever.
This is becoming a more and more adoption issue.
It’s only that most of the businesses already have strong technologies. The challenge for them is to induce change in people, processes, and decision making cultures to move in the direction that they do.
That makes all the difference, doesn’t it?
The Modernization Paradox
I’ve seen companies make this transition from on-premise to cloud-based in very quick time periods. In a sense, it looked great technically;
- Scalable infrastructure
- Automated pipelines
- Centralized data platforms
- AI-enabled analytics
Near real-time reporting
However, after the few months, several business teams were still:
- Exporting dashboards to excel.
- Maintaining shadow spreadsheets
- Requesting manual reconciliations
- Distrusting KPI numbers
- Deciding without understanding or knowledge.
The platform too, had undergone modernization. There had been no making of that behavior.
This is the dilemma a lot of companies are going through today.
There has been a greater rate of change of technology than of organizational change. But the reality is that the changes in technology have outpaced changes in organizations.
Data ecosystems are extremely powerful in the modern world:
- Cloud warehouses easily scale to virtually any extent.
- AI can provide summaries of enterprise knowledge in seconds.
- AI can summarize enterprise knowledge in seconds.
- Real-Time pipelines process millions of events per minute continually
However, this doesn’t necessarily make it a data-driven company.
Data transformation is not simply a “platform transition”. It’s a change of behavior.
And behavioural change is always more difficult.
Just as one might expect, trust, habits, and accessibility are the core of the Real Bottleneck. In a number of modernization programmes, architecture is given a vast amount of attention. Often the adoption is considered as a secondary thought.From there, things start to wane.
1. Trust Issues
Business Groups are not convinced by the Data and they have doubts about the Data. When there are two different numbers for the same KPI on two dashboards, trust goes down the drain in no time at all.
If there’s no longer any confidence:
- Team develops their own reporting logic
- Manual validation increases
- Shadow reporting systems are created.Shadow reporting systems come into existence.
- Adoption quietly declines
A lack of trust is just about the only way to cause fragmentation at scale with modernization.
2. Old Workflows Keep on Winning!
Familiar methods of working are followed.
Despite the use of state-of-the-art analytics platforms, organisations may still continue to use:
- Email-based reporting
- Spreadsheet-driven analysis
- Offline reviews
- Manual approvals
But because workflows were never redesigned to incorporate the innovations — not intentionally resisted, but simply that they were never incorporated.
Technology was upgraded.
There was no operation behaviour.
3. ‘Self-service’ is not always ‘self-service’.
Oftentimes, ‘self-service’ isn’t really ‘self-service’. The term “democratizing data” is used by a number of organisations.
However, technically oriented teams are still very much relied upon by business users for:
- Metric definitions
- Dashboard modifications
- Data interpretation
- Access requests
Any platform that can only be used by specialists isn’t really modern. Accessibility has as much to do with architecture as it does.
AI Complicating the Situation
AI’s now is a way to exacerbate the same issue.AI’s Now is a means to magnify the same issue. Generative AI is driving these to be even more prominent.
Organizations are quickly mobilizing:
- AI assistants
- Enterprise copilots
- Conversational analytics
- Intelligent search
- Automated insights
But many are learning, as with data modernization, that the path of going through an AI journey is not so easy.
Without trust in the information, there will be no trust in the outputs by the users.
Without workflow change, AI is just a passing fad, not a business enabler.AI is a fad without workflow change. Then when governance is poor, AI merely exacerbates the same. While AI can help resolve some of the challenges in adoption, it does not solve all of them. It magnifies them.
The Right Organizations Approach Transformation Differently
The most successful modernization programs consider change as an organisational process, rather than simply a technology change.
This alters priorities quite a lot.
Instead of asking:
- Where to migrate to?
They should be asking:
- How to get data used to be “Frictionless”?
- How can we take into account the insights and incorporate them into our decisions on a day-to-day basis?
- What steps do we take to gain trust between business teams?
- What are the ways of minimising reliance on technical constraints?
There will be a change from infrastructure to enabling. This is where true opportunity gets to begin to come into play.
The actual view of a successful Data Modernization.
The organisations that are making measurable progress typically allocate the same level of resources in four areas:
- Technology: Interoperability, scalable and modern platform solutions.
- Trust: Governance, Quality, Lineage and consistent Business Measures.
- Accessibility: Enhanced business friendly experiences and consumption model.
- Adoption: Training, Change Management, Leadership sponsorship and Workflow integration.
Because ultimately:
The key to the equation is a modern platform, and if it’s not adopted by enough people, it’s still a legacy platform.
This could well be one of the key lessons enterprises will learn in the coming decade.
Human-Centred is the future of Modernization.
Data modernization will be more than an improved infrastructure, or more sophisticated AI models.
- It will be measured by the effectiveness of the organizations in:
- Build trust in data
- Make sense of data and information.
- Use technology in line with business processes.Integrate technology into business processes.
- Develop cultures that are not data-resistant; make it natural, not forced, to use data.
- Not all of the companies who will be successful will have the most advanced platforms.
They will be those that will make data invisible in the most invisible manner — effortless and seamless integration into people’s working, decision making, and collaborating practices, every day.
After all, it’s not until it’s easy to do that it’s actually done.