Rich Pugh at Mango Solutions describes how the synergy between software, data and analytics is the gateway to realising business value.
For years IT teams have sought to store and protect data effectively. As a result, massive IT estates were developed and deployed, all the while providing both guarding gateways and authorities to end users.
Today’s changed conditions require IT and technology to adapt and fast. It’s vital for organisations to utilise all assets effectively across the business, and a key component of this is understanding and delivering value from data. The rebalance from reactive to proactive process requires massive shifts in attitudes and skills and demands a flexible forward-thinking IT strategy that allows data to be transformed into decision making wisdom in a repeatable, sustainable way, available to users at the right time.
As a result, today’s data-centric IT strategies are increasingly seen as crucial to business but are still elusive across organisations. In order to thrive, digital transformation programmes must address not only data but analytics, software, skill sets and mindsets. Without addressing each of these areas, transformation can be short lived, misunderstood and not developed.
Developing high level synergies with a consistent approach towards analytics and data, and the software used, is vital in order to create a digital transformation platform. This concept of ‘synergy’ can be summed up simply by viewing every data project as a software project, and every software project as a data project. In doing so, it’s useful to look at these key components more closely:
The role of data and analytics
Modern data science takes in a broader range of analytic approaches than ever. For example, the role of data and analytics has evolved from a reactive exercise to a strategic discipline. This, in turn, has driven the need for advanced analytics to be combined effectively with software engineering, because if analytics is now an ‘always-on’ capability, businesses need a systemic, intelligent approach that enables solutions to be properly deployed, scaled, supported and extended.
Think of it this way: an ‘old school’, manual or ad hoc process risks missing the point of advanced analytics entirely. For instance, no business can truly describe itself as a ‘data-driven’ business if analytics is run by experts – no matter how capable – armed only with their minds and laptops. What’s more, intelligent decision-making can’t be a process that goes on holiday with the data scientist.
Similarly, any organisation that wants to operate in real time can’t wait for a statistician to analyse the latest data sets to identify new opportunities, risks or react to customer behaviour. Instead, to positively impact a business with data, an end-to-end analytic workflow must employ software engineering principles. This includes everything from the creation of data pipelines and deployment of models, to the creation of user interfaces and applications that can convey insight in the right way, linked directly to operational systems to action and process outcomes.
The role of software
Software plays a key role in providing standardised approaches for using data and analytics across an organisation. As a way of rolling out best practice it can be unrivalled and is an effective way of addressing regulatory and compliance issues around data and its application.
Using software as a vehicle to bring analytics, data and end users together results in an increased ability to leverage advances in data and analytics to create richer, more powerful and more useful experiences and applications. There’s no doubt that any organisation that can broaden its understanding of the possibilities offered by analytics can not only ask better questions of itself and its stakeholders, but then use that insight to build software tools that are truly aligned to a users’ objectives.
Data projects and software projects
Every data project is a software project, and every software project is a data project.
In real world terms, imagine designing a software application for call centre staff speaking with customers. Traditionally, this may have been built on a system that combined data from disparate sources – such as previous orders, interactions and demographic data – to give the call centre professional a single view of the customer.
But by applying data science, functionality can be broadened to include analysis of likely customer churn, which is then linked to suggested retention actions, such as new offers or scripts to help encourage customer loyalty. Similarly, software that works in synergy with data and analytics can route new customer calls to exactly the right service expert, instead of being randomly allocated to the next available agent.
Put in these terms, the contrast between traditional and modern approaches is very apparent. The bottom line is that the use of data and analytics in software can have a transformative effect on the quality and usefulness of our software systems creating win-win situations for customers and organisations.
Ultimately, the application of technology is about achieving business strategy goals, and increasingly, this requires flexible, integrated software and data capabilities to deliver effective digital transformation. Companies that can achieve this will do better than their competitors, grow more quickly and see exponential commercial gain.
Creating synergy between software, data and analytics is no longer a ‘nice to have’ – it is increasingly a minimum requirement in order to deliver business value.
Rich Pugh is Chief Data Scientist at Mango Solutions – an Ascent company
