Data never sleeps and we’re creating more every day.
From social media posts and clickstream data to customer purchase history and financial transactions, organisations now have access to more information than ever before. Data is no longer just a stream; it is an inundation, and it is coming at your enterprise whether you want it or not.
Towards optimal data utilisation: Why you must update your data architecture
Managing this data inundation requires you to constantly enhance your approach to data collection, storage, processing, and insight generation.
You cannot just be focusing on using data to support operational decision-making; first, because that approach is so 2019 and, secondly because almost every one of your competitors is already doing it in some shape or form.
Moreover, the ever-growing complexity of data, cloud, and enterprise processes means that your current data approach may already be outdated and deliver less-than-optimal outcomes.
To truly differentiate yourself, you must begin using data as a strategic asset that can drive growth and competitive advantage.
This can be done by deploying a well-defined, modern data strategy and architecture – and that cannot be achieved until you understand the opportunities that come with modernising your data architecture, as well as the challenges that you may face while undertaking this endeavour.
Opportunities and challenges of data architecture modernisation
There are many benefits to be gained from modernising your data architecture.
For instance, a centralised, well-defined data architecture makes it easier for users to access the data they need at the point of decision from enterprise applications, both on-premise or cloud-based, via seamless data integrations.
This ease of data access is critical for scalability and helps you address issues with data silofication that inevitably arise as your enterprise grows and data gets stored across multiple environments, locations, and formats.
It also helps you save storage and processing costs by eliminating duplicate data, automating processes, and improving efficiency.
Moreover, the time from insight to action reduces substantially, as users no longer have to wait for data science teams to service their queries. This helps your business become more agile and adapt better to real-time changes in the market while making more accurate and contextual decisions.
Data architecture modernisation also allows for the creation of a unified metadata repository that provides a consolidated view of all organisational metadata.
Doing so not only allows users to locate trusted data sources with ease but also minimises the duplication of information.
Further, it standardises data acquisition, processing, storage, and governance to improve the quality and consistency of data across the organisation, thus reducing inaccuracies and misinterpretations that cause business delays and losses.
Of course, no change is ever easy – and the same holds true for any initiative aimed at modernising your enterprise data architecture. The most common of these is user adoption.
Human beings are creatures of habit, and making them move to new or unfamiliar tools and processes can lead to resistance. There may also be teething issues when adopting new technologies and solutions that are required during a data modernisation exercise.
Some enterprise stakeholders might also be daunted by the time or cost required for such a transformation, while others might worry about any business disruption that it may cause.
Thankfully, you can address these concerns by following some basic guidelines.
Recommendations for success: Ensuring seamless modernisation
Before undertaking any data modernisation effort, your first priority should be to get your stakeholders – from sales, operations, and marketing teams to finance IT, and HR – onside. Any business transformation initiative, after all, is only as successful as its adoption by the people who will use it day in, day out.
Securing this stakeholder buy-in becomes easier with communication and training. Communicate the goals for deployments, the changes that will follow, and the results that users can expect.
Address any concerns or doubts and have resources such as FAQs and usage guidelines available to your end-users. It is also advisable to invest in L&D programs to improve data literacy across the board, while specialised training modules can be used to upskill the organisation’s tech teams and make them more familiar with the solutions being deployed.
The next step is the creation of an architecture governance framework to codify the goals and milestones. Doing so allows you to clearly define success metrics – such as increased operational efficiency, cost reductions or even improved customer satisfaction – while also supplying benchmarks to measure the impact of the deployment. Further, having a framework in place allows for swifter identification and resolution of any technical or user-related issues in real-time.
Last but not least, it is critical to remember that modernising your data architecture is an ongoing journey, not a destination. No matter how current your data architecture, the rapid pace of technological advancement will require it to evolve to stay relevant and effective. Therefore, when modernising your data architecture, it is critical to choose solutions that are adaptive, agile, and flexible enough to be upgraded with minimal business disruption.
By 2025, the global data sphere will reach an estimated 175 zettabytes – more than five times it was in 2018!
A well-defined and modern data strategy and architecture can help your business stay on top of this data inundation and tap into the benefits that it brings. That, for any modern business, is the only sustainable approach for long-term success and growth.