Time to rescue your data operations
The value created by data can fundamentally influence key areas of your business, from enabling, optimizing and steering key functions.
Data enablement has been trending for the past few years as businesses have been building “data driven” capabilities and the “digitalization” of everything. The data initiative has become a strong interest area for business executives, and ownership has started to shift away from the core IT organisation.
What is the state of the data initiatives today?
I’ve heard some interesting viewpoints on this....
Business unit head: “We get constantly beaten by the competition who have 10x more value to offer online. We have been waiting to modernize our online service for 2 years. The differentiated value of the new service would be based on the combined data coming from our different units. The quality of the data is inadequate because everyone is building the new things for their own purposes. We´ll have to wait for someone to fix this at an enterprise level before it makes sense to do anything. Right now, it would be too expensive”
HR business partner: “I needed to make some informed HR decisions for Q3 within one specific team. I went to the named BI guy to ask for data. His response was – I am busy right now…come back in 3 weeks and we´ll have a look.”
Head of development: “I don’t know who owns the data initiative from the enterprise perspective. IT is in love with the tech assets they have built years ago, and to me it feels like business units have constantly some new data projects ongoing”.
Gartner: “Data and analytics leaders often struggle to balance the need for both centralized and decentralized data and analytics approaches. Too much centralization stifles flexibility and agility that business domains seek. Although too much decentralization can create chaos, wasted resources, duplication of effort and siloed data create stacks of questionable data, as well as the inability to create trusted insights across different domains” (https://www.gartner.com/en/documents/3970860/how-to-create-data-and-analytics-everywhere-for-everyone)
The same key issues keep appearing in our discussions..
· Narrow use case driven success stories
· Limited availability of data & severe data quality issues
· Architectural complexity constantly increasing
· Very little re-use of assets over use cases
· Exploding cost on enterprise level (Business & IT) – little metrics on value
· Data initiatives fail to deliver the business outcomes
· Risking security & compliance
· Dozens of technologies in use and more coming in
· Massive workload on delivering the basic capabilities
· Shortage of talent as an outcome of the complexity
In our data initiatives, we have identified that most organisations are facing challenges due to their current approach, focused on use case specific implementations. We have realized that in order for data initiatives to provide more value, this needs to be addressed now and addressed holistically.
We’ve identified the situation to be similar to the early days of cloud adoption, i.e. business units taking cloud into use for their specific use cases, different environments accumulating within companies, eventually leading to chaos. We solved that chaos by helping customers build an operating model around cloud services and deploying a common set of services that ensure compliant, secure and cost-efficient operations without sacrificing agility. We have taken a similar approach to tackle the emerging data chaos with Data Estate Modernisation.
How do we tackle Data Estate Modernisation?
We help our customers take control of their overall data initiative ensuring value creation is stronger than it has ever been, and the operation is fully optimized. We implement an operating model backed up by the latest tooling capabilities. This enables onboarding new data initiatives in a fast manner, safeguarding the entire operations from security, compliance and cost perspective.
To achieve that, we have defined three journeys based on the angle of entry:
- The data value track which is focused at identifying business opportunities around data and building the capabilities required using Azure analytics services
- The data foundation track which is aimed at building the holistic capabilities (operating model + technology) on Azure data services to enable data driven operations
- The data modernisation track which is aimed at modernizing existing database technologies to Azure to drive cost savings
The data enablement track ensures that there is a holistic model and platform for managing data from different sources, used for different use cases from the business.
We recommend to start by aligning the stakeholders on key principles on data operations and define a shared data operating model. This ensures that the business objectives, responsibilities around data onboarding, consumption, security and compliance are clear to all relevant stakeholders.
Once the responsibilities and key principles are clear, the next logical step is to use data services available on Azure. Ideally you need a specific business use case in mind (i.e. Minimum Viable Product), but understanding that you should implement on the basis that you are ready to onboard the next use cases with minimal impact on the setup. I.e. think more of “generic” services rather than implementations optimized for one specific use case. The evolution of this “data foundation” proceeds this MVP stage in an iterative way, by adding in use cases and data sources.
It is key to ensure that the data initiative is tightly connected to business outcomes and driven by clear business objectives rather than driving the initiative as an IT “capabilities”- project disconnected from real business cases. For example in the AI domain, proof-of-concepts have been failing to provide the value expected to businesses, which has led to only a minimal amount of PoC ever seeing light as production solutions. The issue is that these AI initiatives have been driven from a capability perspective (adoption of AI) rather than business outcomes.
We also have seen examples of IT organizations building state of the art data capabilities which have not been adopted by the business for broader use. It is always easier to change when you have been part of planning the change.
On our data value journey, there are three stages described to tackle the challenge above:
- Genius workshop - identify the relevant and most impactful data opportunities for your business
- Proof-of-value - put the objectives to a test and prove that the set objectives can be achieved with data
- Production project - instrument the proof-of-value to a production grade solution that is embedded to your operations and ensures continuous value production
In many cases there are already existing solutions in the data domain. These solutions may be outdated, i.e. not capable to support the data velocity, variety and volume expectations from today’s businesses, or expensive compared to the cloud native alternatives available on Microsoft Azure. The data modernisation track helps customers modernise their current data tooling to modern Azure based alternatives. This drives better performance at a lower cost. We’ve seen a lot of customers moving from old-on-premise based Microsoft SQL Server based solutions to managed cloud, but also customers migrating from 3rd party database engines such as Oracle to Azure data services such as Azure SQL, to ensure low operating costs and maximum availability / reliability.
It is rare to have just data without it being connected to applications that produce or consume it. Therefore, modernisation initiatives often require you to take related applications into account. You need to carefully assess the related application landscape, identify changes required by the data modernisation initiative, and plan for those. Typically the data estate modernisation initiative consists of not only the database / data technology modernisation but also has an aspect of application modernisation.
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