The Truth Behind Digital Transformation

data lake concept

The Digital Transformation Revolution

What is the truth behind the marketing gloss of the digital transformation explosion? What advantages and challenges are created by going digital? We consider the fundamental benefits that sit below the more obvious operational advantages and explain how new categories of data can empower companies provided strong governance is applied to their usage.

Digital Transformation

Digital Transformation can be defined as the migration from traditional workflow processes and legacy systems to digital, Cloud-based solutions. Examples include Enterprise Resource Planning, Unified Communications, Customer Relationship Management [CRM], Trade Execution and Order Management systems [OMS]. These new systems operate from the Cloud, be it public or private.

Cloud solutions, and the tools that surround them, have had a massive impact for business and the operational benefits have become self-evident during the COVID-19 pandemic. Well-prepared businesses have been able to easily relocate their workforce from offices to homeworking settings. For others, rapid deployment and migration has happened over the last few months. For the least prepared, a temporary hybrid of systems and services has been pulled together with the hope this will get them through the present situation.

The Benefits of New Data

Beyond operational flexibility, one of the prime benefits of digital systems is the vast amount of data that is generated. Such data goes beyond the obvious searchable categories that we are all familiar with. It also includes new data classes that help us to contextualise the bulk. These different sorts of data are referred to as structured or “light” data and unstructured or “dark” data. As a general guide, structured “light” data is self-explanatory i.e. you can identify its purpose and context, and it resides within a designated field – for example in the database of a CRM or OMS. Unstructured “dark” data includes e-mails, documents, free-form text, and recordings of conversations. It also encompasses the metadata that systems generate as communication, activity, and system performance logs. This typically resides somewhere less searchable within your Cloud and would not be accessed by an end user.

Although there are challenges, digital solutions enable you to harvest dark data generated by component systems. Analysing it can enable you to:

  • Review the cycle of engagements and the transactions therein.

  • Automatically analyse your digital workflow so you can rapidly implement change to your business operating model
  • Achieve regulatory compliance across entire workflows
  • Identify new revenue opportunities in a sea of untapped data.

Big businesses have decided that these benefits are worth the challenges involved. We will come to those challenges shortly.

Ethical Governance

Despite the benefits, we must be mindful of the nature of the data we have collected. This will include sensitive and critical information that needs appropriate security protection to ensure regulatory compliance. Some businesses have utilised data to analyse the working habits of employees. Such analysis has involved tracking how long they spend on each activity, and how long on breaks; enabling scoring on individual efficiency and productivity, and highlighting any unaccounted activities.

We must heed caution in adopting this approach to data analysis. Ultimately, a business’s reputation rests with its employees. A balance must be struck where surveillance protects against regulatory risk without dehumanising the workforce and creating a culture of constant, critical observation. The media has been unforgiving to businesses that get this wrong, moving quickly to publicly name and shame them.

Data harvesting can be a very powerful tool for business, but it should be used in a way that enhances a positive culture. Over the last five months of lockdown, we have seen employees demonstrate that working remotely need not interrupt business operations. This has been accomplished by adapting the traditional office-based operating model and has required a commitment to productive two-way ‘remote’ relationships between employer and employee.

Ecosystems Strategy

Having established good governance, the challenge remains as to how to gather and normalise the data to work together. A coherent ecosystems strategy can help to simplify this task. The complexity of modern service requirements means that a single Cloud provider is unlikely to be viable. So, businesses need to select a portfolio of systems that add tangible value to their operations. Getting such ecosystems right is about understanding the business objectives and workflows. The company needs to consider what data it holds (sensitive or otherwise), what it wants to learn from the data and how it can be used to improve commercial, operational, and regulatory performance.

Data access and alignment should be considered as part of the selection process but there is complexity in the array of languages and formats, be it different written languages or data format such as voice or video recordings. Different formats need to be normalised (transcribed) to be a part of the data lake. The integrity of source data should be protected by maintaining it in its original state, with the normalised data being created in a separate lake.

Data Analysis

New products and services built on artificial intelligence, machine learning and natural language processing can help to analyse and interpret the normalised data. They typically have a narrow area of focus. This is unsurprising given the expert knowledge needed and the inherent lexicons of language, composed of jargon, acronyms, and forms of syntax. Different products can be employed to answer different questions, potentially by interrogating the same data lake.

There are also companies that focus on the normalisation of data, especially in the complex categories of voice and video. This data requires unique skills and accurate output requires suitable budgets and resources. Although AV data may only account for 20% of your data lake, it may well represent the most significant and useful part. An ecosystems approach is needed to manage all stages of data management from capture, to normalisation, to analysis.

Some large corporations are developing in-house products that will answer questions specific to their businesses. Given the ever-evolving market, it is important that an ecosystems strategy enables businesses to flip from one product easily without heavy costs and time-consuming projects. Flexibility can be maintained by prioritising consumption pricing models over perpetual licensing.

In this new era of digital, businesses will embed this technology across all elements of their business units. In doing so, the responsibility for data shifts from CIOs and Chief Data Officers to the entire Senior Management Team. Data access control requires vigilant governance to ensure compliance with regulations such as GDPR, MiFID II, Dodd-Frank, KYC, FCA, SEC, and standards such ISO.

The journey of working with digital systems gives us so many opportunities to enhance service offerings. We must, however, be cognisant that in our business activities we are still humans selling services to humans. The path we take towards the digital revolution must have at its heart the culture and community that the business itself represents.

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