Information overload

Big Data and Augmented Analytics

Information overload:
Big Data and Augmented Analytics

Big Data is everywhere, but how can it be transformed from pure overload into workable insights and knowledge-led action? Can Augmented Analytics tools help organisations move towards much needed agility and transformation?

Usman Wahid and Isabel Simpson, Commercial Technology & Data, give their thoughts on the opportunities that Big Data can bring.

By now we’ve all heard of the term ‘Big Data’ and are likely au fait with ‘Augmented Analytics’ as a concept too. Data – as information-gathering and working with it – is at the heart of both ideas. Most of us understand data as a day-to-day consideration. We’ve come to recognise the prevalence of data in our scaled-up, rapidly advancing digital world and acknowledge the part we play in adding to it, giving away our own data in return for convenience and seamless transactions online.

The true market value of data collection is hard to assess, particularly when we conceive the continuous streams of data amassing in great volumes and at speed. Big Data indeed, but to what end? Some of the language around data verges on the environmentally apocalyptic: talk abounds of data mining, data lakes, data leakage, data erosion, data forecasting, and data as the new gold, or as the new coal fuelling insatiable human consumption. Big, raw data sounds like hard work, especially if it is without refinement and a clear strategy and purpose.

Big Data: Actually quite useful

‘Big Data’ is best conceptualised as the collection and storage of all types of structured, semi-structured, and unstructured data by companies and organisations. Data such as this is stored in large amounts and with such complexity that traditional data management tools are not up to the job of processing it. The term refers to volume of data, which is at unprecedented levels and shows no sign of slowing.

Three ‘v’s characterise what Big Data essentially stands for: volume, variety, and velocity. But while data of every kind is available for the gathering everywhere, there is little value (another ‘v’ right there) in collecting and storing data purely for data’s sake.

The rules around storage and retention of data are complex. While it’s been a longstanding principle of European data privacy law that data should be held “no longer than is necessary”, the UK GDPR does not specify exactly what that means in terms of actual timescales. This makes sense, as the periods for which you can keep data without falling foul of data privacy rules are context specific. In theory, keeping data “no longer than is necessary” translates neatly to the storing and processing of data just long enough to allow execution of the purpose for which data was originally collected. However, in practice, this is problematic as it assumes each data element is collected for a single purpose, which is seldom the case.

Data protection issues aside, what matters most for meaningful Big Data collection is the way it is interpreted and used, with accuracy and aggregation being key to its value proposition.

Augmented Analytics

Intrinsically linked to Big Data, Augmented Analytics (AA) are highly advanced data analytics, often fuelled by Artificial Intelligence (AI) technologies to help make sense of – and help work with –enormously complex and varied streams of data. It describes the process of examining captured data to uncover patterns, trends, and other types of information that can, in turn, provide valuable insights which help organisations improve their operations and make better-informed decisions.

It’s a massive market: research company Gartner noted at its Data & Analytics Summit 2022 that “82% of organisations have already adopted augmented analytics or plan to before the end of 2022”, while the global market for AA is forecasted to reach USD $29.86 billion by 2025, according to ‘Global Augmented Analytics Market’, a 2019 Allied Market Research report.

The data-gathering of old

Traditionally, the world of data analytics belonged to data professionals trained in the science of data and who had the requisite software needed for making sense of the data noise. This work took time, and organisations had no choice but to wait for the processing of data before it could be used to guide their decisions and actions.

That was then, and this is now: decisionmakers cannot afford to wait that long, while change has become rapid and the competition won’t slow down. Leaders of organisations demand deeper insights, more of them, and at pace. Yet most data science teams cannot scale their operations fast enough to keep up with the demands for data analysis. Enter AA – tools for the most modern organisation.

How do AA work?

In simple terms, AA harnesses technology by employing intelligent tools to cut out the arduous tasks of processing, aggregating, and visualising Big Data. AA can be used across an organisation, leading to an added benefit of data democratisation. Individuals, departments, and teams can learn from and develop insights into data and are empowered to make faster data-driven decisions. This in turn increases firm-wide data literacy, and over time, the organisational culture shifts to teams becoming comfortable working with data, learning to collaborate, and creating business value with it.

There are three main components of AA:

Machine learning (ML). A type of AI, ML uses algorithms to rapidly search historical data, identify patterns and deviations and generate insights and recommendations. ML models thrive on Big Data, continuously learning from new structured and unstructured data without human intervention.

Natural language technologies. Natural language processing allows humans and computers to talk with one another more easily through the interpretation of human language for computers, while natural language generation translates computer code into human language.

Automation. ML-driven technologies automate routine manual tasks across the data analytics process, significantly reducing the time needed to build, train, and deploy ML models.

Benefits of AA

AA allows more immediate access to data, helping organisations make faster, better business decisions. It takes manual labour out of the job (the data scientists of old are freed up to do more innovative things with their time) and eliminates the potential for human error. It paints a bigger overall picture of a situation, combining disparate parts of a business or a process together rather than having company data exist inefficiently on different platforms. Reporting and tracking systems are brought together through data integration points which has many benefits, such as streamlining customer journeys.

If used effectively, AA enables organisations to implement faster response times and implement intelligent cost-effective resourcing, reducing their cost base and potentially increasing profitability by removing human costs and inefficiencies. Organisations that implement this level of data literacy consistently and effectively may increase their overall revenue and efficiencies. In the current socio-economic climate, any action that increases efficiencies and retains clients is an attractive prospect.

AA help organisations take greater ownership of data and the subsequent generation of actionable insights. It is useful for consolidating specific metrics, KPIs and other information into customised data dashboard and reports, making complex data more comprehensible. It identifies the most important signals in our data, helping filter out the noise and enabling people to focus on what they are good at.

As AI becomes more sophisticated, synthetic data – data that is manufactured for use in AI models – will become an increasing component in the data landscape, helping to ensure data stays an asset rather than a liability. At Gartner’s Data & Analytics Summit 2022, the market research company estimated that by 2030, synthetic data will completely overshadow real data in AI models. One clear benefit of synthetic data is that it neatly side-steps issues of data protection. As made clear at its Data & Analytics Summit 2022, Gartner predicts that by 2025 synthetic data will enable organisations to avoid 70 percent of privacy violation sanctions.

What about the humans, though?

AA is not a replacement of human intelligence. Rather, as the moniker suggests, it augments human intelligence, intuition, and curiosity. ML is a great example of the importance and influence of the human mind, as it is based on taking contextual and behavioural cues gathered over time from users, assessing human intent and preferences and offering appropriate insights, guidance, and recommendations through natural language. The actual decision-making is then left to real people.

Leveraging Big Data and AA

There are specific ways organisations can leverage Big Data and AA to make an impact, sharing data and insights to encourage change, and help with decision-making.

Firstly, it is important that insights are delivered at the right time. Leaders want to make decisions quickly and reactively, and AA will help them do so. Faster analytics can actively signpost issues, opportunities, and challenges, and clearly link data outputs with impactful decisions.

Secondly, aim to accelerate your decisions with automation. Respond to questions with relevant insights in real time, on-demand.

Thirdly, connect your decisions across different people, teams, and functions to help with the overarching picture. Share and learn, leveraging your knowledge for the greater organisational good. Your customers, clients and colleagues will thank you for it.

Faster analytics can actively signpost issues, opportunities, and challenges, and clearly link data outputs with impactful decisions.

Benefits of Big Data and AA for the legal profession

The legal system is founded upon research tasks which are labour-intensive and time-consuming. There’s a gleaming opportunity for AI-enhanced AA tools to carry out tedious legal research, lighten the load of courts and legal firms, and speed up the judicial process. Current Big Data tech enables lawyers to plan their schedules efficiently, reducing time and resource wastage, while analytical tools of judicial precedents and interpretation of legislation are critical for lawyers in preparing their cases better. Predictive analytics represents the cutting edge of the AA market, which allows the prediction of outcomes via statistical modelling.

These attributes have carry-on benefits for clients looking to source more cost-effective ways to manage their legal spend and allows lawyers to add value for clients in the right areas.

Big Data combined with AA gives lawyers instant access to the information they need, helping them make predictions about the outcome of a particular case or to accurately calculate fees. This contributes to enhanced efficiency and cost reductions, saving time and money.

Benefits of Big Data and AA for clients

These attributes have carry-on benefits for clients looking to source more cost-effective ways to manage their legal spend and allows lawyers to add value for clients in the right areas.

Big Data and AA are conduits for better decision-making; led by metrics, not gut instinct, evidence rather than memory, and innovation in place of lagging tradition. While the volume, velocity and variance of data is certain to continue and to increase, organisations must understand the real value of this precious material itself. Transforming data overflow into insights, creating efficiencies, and enriching the knowledge base of the organisation are benefits that extend well beyond clients into the wider business and beyond.