Pushing agility in your data strategy to power up business

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Accurate data has long been a component of budgeting and planning. But today, strategic planning is not a periodic ritual: it requires accurate, up to the minute information. Hence, the underlying analytics must be seamless, digital and fast moving to keep up.

We looked at tactical ways to do this. As part of our global remit, we created a composable digital playbook of entire platforms, code and design frameworks and components. Our teams can now morph digital structures to meet specific business use cases by building custom platforms assembled from base frameworks taken from a common stack. In this way our teams can resolve many issues through essentially crafting custom platforms in a way that is similar to selecting shifting patterns in a kaleidoscope. This adaptability helps enterprises to remain competitive and allows them to share expertise across verticals internally.

Why you need modern data platforms: positioning your business

AI brings further possibilities for orchestrating insights and responses.

Today, modern data platforms offer the basis for purposefully bringing worlds closer through incorporating AI with other frameworks, opening up avenues for meaningful integrations between disparate data sets and federated ecosystems. Here, we can see how emerging technology is making it possible for multiple aggregations and intelligent comparisons to be done in real-time to accelerate our actions in a continuously fast-moving world landscape.

The composable digital stack we advocate, and are putting into place, will help address these challenges. At a conceptual level, it takes the form of a multi-layered cube with the ability to abstract and check code and digital structures at the different levels — including infrastructure, software code, applications, platforms, storage, analytics, design and AI, contributing to the dynamic design of a strategic thriving enterprise.

Leading players in tech, retail, media & comms, banking, healthcare & pharma, cyber-security and international organisations who can leverage this successfully will stand out from the crowd by operating from strong technology and business fundamentals. For example, the Ocean Cleanup project integrates data from multiple data sources collected world-wide from international and local bodies as well as members of the public and research institutions.

Legacy business technology challenges and how modern data platforms overcome them

Organisations that don’t respond to changes with agility and do not change their technology strategy to use modern data platforms face three common challenges that will typically impede organisations with business models based on outdated data architectures:

1. Integrating multiple data sources and use cases: while new platforms that are involved in delivering analytics at scale are designed for this kind of work, older data platforms have evolved over time and are not prepared to manage more modern sources of information, such as machine to machine (M2M) data
2. Rapid scaling to handle large datasets: often in legacy setups, the enterprise pays over the odds for capacity, even if it’s unused. However, cutting back on capacity outright is high-risk and can leave the company unprepared for streamlining and rapidly scaling, as it needs time to upscale necessary hardware and software. By then, the opportunity may very likely be lost to a more agile competitor
3. Speed response times and eliminating reporting bottleneck: legacy reporting can lead to an inability to discover new insights rapidly when handling distributed and large datasets and ecosystems due to cumbersome workflows, and requires highly specialized IT resources. This can result in long lead times to obtain and ingest new sources of information. Without agile tools to create reports themselves, users can wait weeks in a queue for IT resources to become available. Bottlenecks in research can slow innovations and delay decisions for senior executives and front-line operatives in accessing high quality MI and their response to urgent and changing operational situations

We need modern data platforms using sophisticated data analytics to drive value from the company’s data holdings using a semantic layer with efficient governance. This will overcome complexity and latency challenges and provide critical decision-making capability to business users, instead of trying to operate a massive data blob that is both cumbersome and difficult to interrogate.

In the past, local teams or countries resorting to their own data tracking as they struggled to build new use cases from the ground up has led to information silos, protectionism and mistrust, creating inefficiencies and duplication of effort and resulting in a proliferation of sources/different versions of truth. The composable stack approach combats this by offering a real opportunity to leverage shared central and local intelligence to address new scenarios at pace.

The modern data platform

Enterprises now have more options than they did before. With modern data platforms, businesses can build flexible engines and systems which offer more agility than traditional solutions architectures, making data accessible to wider audiences. Here are some guidelines to help distinguish a modern solution from a more traditional hard-wired enterprise model:

• Results-driven: a modern data architecture is geared to be business centric, not IT focused. It isn’t about technology for the sake of technology or hard-wired structures, but about driving better business outcomes. It is focused on the needs of the business over the technology that enables it to power business success and adaptability
• Automated: next, a modern data architecture leverages automation. It seeks to augment and automate the most manual tasks to ensure we are not building brittle processes
• Flexible and elastic: a modern system should be flexible enough to address use cases which aren’t even envisioned yet. A modern data architecture is also elastic, leveraging the power of cloud computing to provide instant, on-demand scalability, ensuring that the right capacity is always available

Agile principles and engineering fundamentally place modern data platforms at the heart of business strategy. The businesses that do this are placed distinctly at an advantage in today’s market.