Yet here we are in 2021 having the same discussions we had in 2011. However, things have changed considerably in the intervening decade, and there’s every reason to believe that this time around, data modernisation discussions will lead to action.
A new world requires a new approach
Several trends are combining to push data modernisation to the very top of the corporate agenda. These include:
- Tech refresh: In sectors like banking and telecoms, numerous companies invested in data warehouses and Hadoop a decade ago to leverage “big data” analytics. These investments are coming to the end of their life, and these businesses are looking to the next generation of data technologies to unlock value from their data. Other sectors, such as retail, have fewer legacy data technologies to deal with, but they can see the value of data modernisation and are looking to catch up.
- The maturity of cloud providers: The services offered by cloud hyperscalers have reached maturity and are now proven across a large and fast-growing range of business deployments that include data modernisation. For cautious businesses that have taken a “wait and see” approach to the cloud, the time for waiting has come to an end. While a modern data foundation can be built anywhere, it is best suited to reside on cloud. The fact is, all the most advanced tools for understanding, analysing, and consuming data are predominantly available on the cloud. Leading companies are using the cloud to reimagine the role of data, and new capabilities are continually being developed by all the major cloud providers.
- The commoditisation of tech: In the past, businesses could differentiate through hardware and software. In the cloud world, where technology is hosted and shared, this is increasingly difficult. Data therefore is becoming more important as a differentiator; both the types of data collected and how that data is put to work in enterprise use cases.
- The need for real-time data analytics and automated actions: Business agility has become increasingly important over the past 20 years as digital disruption mandated frequent change. COVID-19 has underscored the importance of agility, and the need for real-time analytics to be able to keep abreast of fast-changing events and make important business decisions quickly. It’s little surprise that, as Gartner found, 66% of companies have increased or maintained AI investments since the onset of COVID-19.
- Changing customer demand: COVID-19 is also changing the way people and businesses consume services as digital has seen a decade worth of growth compressed into a few months. Businesses that can best leverage AI and data to create compelling new experiences will be best placed to thrive in the new environment that emerges following the pandemic.
“A perfect storm of factors has come together to make data modernisation a top priority. Businesses realise that they need timely data insights more than ever to keep pace with changing consumer behaviours and to navigate unpredictable macroeconomic conditions. It’s become clear that only the cloud can deliver the scale and agility needed to analyse data in real-time and to move from insights to wisdom. Businesses also see the cloud as a way to reduce the cost of data management and to free them from time-consuming practices so they can focus on higher value data science activities.”
- Marco Tranquillin, EMEA CE Practice Lead for Data Analytics at Google Cloud
Barriers to data modernisation
As businesses look to modernise their data they’re coming up against a range of barriers. For organisations that have yet to start in earnest on data modernisation, the most significant challenge is that legacy systems don’t support data analytics at scale. Siloed data platforms and workloads make it difficult to combine and standardise data and run advanced analytics against it. Without this ability, valuable business insights remain hidden.
For more mature businesses, the challenge is one of complexity: how to manage data across multi-cloud and hybrid environments. While the goal of AI and analytics should be to use data holistically, hybrid environments require that certain workloads remain in certain locations (because of, for example, regulations around data residency) while other workloads will be able to move around more freely.
There are also the related issues of privacy, ethics and trust. Regulators and citizens increasingly expect data to be secured appropriately and for personal data to be protected at all costs. Businesses looking to leverage such data therefore need to ensure they have put in place full data lineage and where that data is used in AI models that impact customers, the AI needs to be able to “explain” how given outcomes are reached. Good data governance must therefore be pervasive across the organisation.
An approach to data modernisation
How can businesses deliver data modernisation in a way that allows them to meet their business needs while also overcoming the challenges we’ve outlined?
The key is to treat data modernisation as just that: a data project, not only a technology project. While technology is involved, it is only as a means to the end of rearchitecting data platform/ecosystem and data models. We see three stages to the data modernisation journey:
- Set the stage. In this phase you need to address the cultural change that will be required to make data modernisation a success, staring by assessing your current data maturity to understand gaps and opportunities. Your people need to be upskilled so that they’re data literate, and tools put in place to democratise data through intuitive interfaces so that all people in the business can leverage it for value creation. New data organisations need to be established within the company and empowered to lead change. The aim should be to turn the enterprise into a three-factor operation where equal weight is given to the business, technology and data.
- Make the move. In this phase, you need to consider how best to build a set of data products that can access and use all the data required, and which are managed and governed. This is the data foundation on which firms can then look to rapidly build out and scale use cases that are trusted and compliant.
- Operate and optimise. Having met phases one and two, the data-enabled organisation can then grow through three levels of maturity. From the basics (using data to improve the core business) to ecosystem plays (using data to connect with other businesses for joint customer propositions) to business innovation (coming up with completely new data-centric business models). In this phase the aim should be to automate data governance, infuse AL and ML into data management and to automate the processes for data operation.
Business should also focus on building a pipeline of skilled data engineers. Here, initiatives such as Accenture’s Cloud First + Data Academy with Google Cloud can be used to train graduates so that they are fluent in cloud native approaches from the outset.
“Businesses do not need to undertake the data modernisation journey alone. Working with cloud providers like Google Cloud and business partners like Accenture, firms can draw on an ecosystem of cloud and data expertise, and hands-on thinking to engineer ideas that solve the most pressing issues facing them. As we’re seeing right across digital business transformation, companies that leverage the right partner ecosystems are able to move faster and be more innovative than those that go it alone.”
- Yann Lepant, Managing Director at Accenture
Partner for modernisation success
The long-anticipated modernisation of data is now well underway. Collaborations like Cloud Design from Accenture and Google Cloud can help enterprises on this journey, offering the right tools, processes and expertise to accelerate the move to a data-centric business. Businesses that get started first and complete their transformations soonest will be better able to anticipate customer needs and build the new and relevant digital experiences that will help them to thrive in the years ahead.