There is a huge buzz around AI and for about 10% of businesses, it’s already being used to give better faster business decision-making processes.
MEDIA 7: Could you please tell us a little bit about yourself and what made you choose this career path?
CHARLES SOUTHWOOD: Like several friends and colleagues who now work in IT, I started my career as a civil engineer and moved into IT as the industry matured. Over the years I have found that many of the attributes that come from that early engineering background are also well-suited to the logical and analytical approaches needed for solving customer business problems with data and IT solutions and to the efficient operation of a sales and marketing business. After graduating from Imperial College, I spent several years in the industry with Taylor Woodrow both on-site and in the design office. This included a unique opportunity to spend several months in the Amazon jungle designing and building bridges to connect remote villages that would otherwise be cut off during the rainy season; both an unusual and very rewarding experience for someone in their mid-twenties!
Upon returning to the UK I moved into the design office, designing dams, oil rigs, power stations and river diversion schemes. Much of the design included the use of CAD/CAM systems and finite difference and finite element analysis (using large matrix manipulation) to model loadings and stresses in structures and to predicting fluid flow patterns. Although business-related computing was in its infancy, I could see enormous potential and found the prospects for much wider use of IT to be very enticing. An opportunity presented itself to move to a company that sold solutions and consultancy services both for engineering and for general business applications. I joined the company in a sales position and although I still had my links to engineering, the faster growth in use of IT for business inevitably led to my focus on this area. This in turn led to other sales positions and into sales management and then wider business leadership roles.
Over the last 25 years, I have therefore had the pleasure of working in a number of software houses, driving the sales and marketing operations of companies where the predominant focus is on innovative market solutions, often disruptive in their nature. I am privileged to have had the opportunity to discuss business issues and strategies with a vast number of large and mid-sized organisations, across different industries and with varying stages of IT and data maturity. It is great to be able to discuss the ‘big-picture’ perspectives on the trends and needs of those client businesses and to subsequently see the impact that can be achieved by supporting them with the right solutions. For the last 5 years, as Regional Vice President of Denodo Technologies, I’ve been responsible for leading the company’s business growth across northern Europe, the Middle East and Africa.
With ever-increasing data volumes, a greater variety of data formats and more disparate locations coupled with the mounting desire for real-time data insights we’ve seen rapid growth in demand for improved data integration. As the world’s market leader in data virtualisation we’re seeing strong adoption from all sectors, addressing the applications integration requirements and data integration needs for a wide array of use cases from self-service and agile BI to Customer360, cloud migration, applications integration and compliance/risk. It is an exciting time to be at the heart of these industry initiatives!
M7: Your award-winning data virtualization platform is regularly featured in the world's leading business and IT publications. What are the core values that drive the company to be a leader in data virtualization?
CS: Denodo was founded in 1999 and has been focused on providing data virtualisation throughout that time. In fact, many look upon Denodo as the ‘founding father’ of data virtualisation technology and the Denodo Platform capabilities, as you say, allow it to be placed consistently in leadership categories of the various analyst reports on data and applications integration. For example, in Gartner’s latest Magic Quadrant for Data Integration (June 2021) Denodo is listed as the fastest growing vendor in the Leadership Quadrant. The company is privately owned, headquartered in Palo Alto and is led by its founder and CEO, Angel Vina.
Financially, Denodo is in great shape with some 50-60% year-on-year revenue growth and a business that is both profitable and has no long-term debt. The combination of strength from consistent executive leadership coupled with strong financials means that we’ve been able to enjoy an unrivalled focus on ‘excellence’; excellence in technical capability, excellence in innovation and excellence in customer satisfaction. This is reflected in the vibrant and enthusiastic user community for Denodo, which can be seen in the many references and case studies available on Denodo’s web site as well as the customer satisfaction ratings in the various vendor surveys.
It is no longer practical to move and copy all the data that might be needed into one large curated data warehouse or data lake. It is too slow, too complex and too costly.
M7: Given the changes in today’s rapidly evolving marketplace, what are the key 'climatic' changes taking place in Data Virtualization today? What systematic approach are you using to address these changes?
CS: When talking to businesses about their wider needs from data integration, the challenges often include siloed data, legacy applications (often designed with no integration in mind), cloud migration and hybrid cloud (on-premise and cloud combinations) or multi-cloud combination, ungoverned ‘shadow IT’ and the wide variety of formats and protocols. Combine this with the difficulties of the rapidly growing data volumes, the disparate nature of modern data stores (on -premise, SaaS, and various cloud locations plus perhaps external sources) as well as the business demand for real-time or near real-time data insights and you have some big challenges! If you are to refer to these as ‘climatic changes’ then most businesses are encountering a heat-wave! This is typically when Denodo gets invited to the party.It is no longer practical to move and copy all the data that might be needed into one large curated data warehouse or data lake. It is too slow, too complex and too costly. The industry is littered with stories of the difficulties encountered with the move-and-copy approach and big data lake projects that never end.
Instead, connecting a virtual layer to all potential sources (without moving and copying the data) means that data consumers can be connected to just the virtual layer and get secure access to all they might need regardless of the location or format. The integration can be provided in real-time inside the virtual layer. It saves on the complexity and cost of all the ‘heavy lifting’ and duplication of vast amounts of data and the users get the agility and real-time data (rather than the batch overnight versions they were getting from physical data warehouses). Of course, IT also benefits from a single layer for audit, security and governance, ensuring compliance and much faster data access for the business. We all do something similar in the home when we watch movies or listen to music with services like Netflix or Spotify. We get what we want when we want it and don’t have to worry about where it is stored. We also get an almost infinite choice as there is no need to hold all the DVDs or CD collections locally, on our shelves. Data virtualisation provides the same kind of paradigm but for business. When it comes to data, ‘Connect, don’t collect’!
M7: Could you please help our readers understand the relationship between data virtualization and digital transformation?
CS: This is a very good question as it is easy to mix the terms of data virtualisation and digital transformation! Digital transformation is the strategic adoption of digital technology to meet changing market and business needs. It often involves the creation of new business processes or the modification of existing ones. As organisations reduce or eliminate paper-based processes we see the switch from analogue to digital. It brings with it new opportunities and new customer interactions that would previously have been impossible.Interestingly data virtualisation is often used in digital transformation projects, as a key element in the new digitised architecture, giving greater agility to the business, with new products, processes and services available through the combination of data in new and innovative ways. Take for example a new omnichannel service to customer interaction, offering a mobile app, web-based access and call centre service as well as perhaps internal management reports and real-time executive dashboards.
These different data consumers require different technologies and therefore different formats and data delivery protocols. Without data virtualisation each different flavour of consumer would need specific interfaces and manipulation of the data to provide the right exposure. This is time-consuming, requires coding and data movement and is both costly and potentially error-prone. It also makes it hard to make changes in a quick and agile manner. However, by using a data virtualisation layer, all the data combinations and transformations can be defined just once, linking the published data to the different data consumers from the same single data model. Now you have a very agile, easy to maintain system with consistent data to all consumers irrespective of the technology. Oh, and let’s not forget it is now also real-time or near real-time from the source! For many businesses, this can be truly transformational!
Many AI applications have obvious and direct benefits which most people would accept as reasonable but as the level of sophistication grows the issues will inevitably be more complex.
M7: How has COVID-19 affected the market? What was Denodo’s strategy during the pandemic?
CS: The demand for data virtualisation is driven by projects and strategies requiring faster, more digital business models where data is recognised as having the potential to be a key differentiator in the business; new insights, faster interactions, new products and services, greater customer intelligence, propensity models, improved governance and compliance, reduced risk and the adoption of AI/ML are all typical business drivers for data virtualisation. The solution is very much industry-agnostic and is therefore used widely across most major business sectors including finance, telco, pharma, manufacturing, government, retail and utilities. For this reason, we saw some sectors accelerating their adoption and others holding back. In banking, for example, the branch closures drove greater digitisation and the adoption of online banking and likewise, the move to a virtually cashless economy provided far more comprehensive data sets for the analysis of customer spending patterns than could ever have been achieved when cash was in play.
For our part, Denodo was keen to help combat the global challenges we all faced. We, therefore, spun up the data portal with the sole aim of integrating disparate global data about COVID-19, curating them, and then providing them free of charge to data consumers such as data scientists, analysts, and researchers, who could use them to look at the impact and potential solutions to this disease. The portal operated for some 15 months, with categories of content from several 100 official data sources from around the world (such as NHS England, ONS, WHO etc) for everyone to consume. It was set up and running in just 21 days and demonstrates the agility available from using data virtualisation for analytics. Some views were accessed 10s of 1,000s of times over its period of operation. The object has been to use data insights to help whoever and wherever in the world to fight COVID-19. It wasn’t just medical data; other examples include the ability to highlight the per cent change in visits to places like grocery stores and parks within a geographic area. Another being the ability to look at changes in air quality across regions over time, based on changing citizen movements. Collaboration was needed to maximise the value of this initiative to society and the portal statistics indicate that it was put to good use over the last 18 months. It’s a great example of the use of data virtualisation for high-velocity high volume analytics.
We’ve also had feedback from customers on their own application of the Denodo Platform during the pandemic. NHS National Services Scotland for example used Denodo’s technology to solve challenges, such as:
-The provisioning of data for COVID reporting to the Scottish Government and the First Minister in Scotland
-Helping to monitor oxygen availability at various hospitals
-Enabling data for Test and Trace of COVID patients and general public
-Implementing the vaccination programme, including venue and appointment management, eligibility, uptake, etc.
-The planning and availability of required PPE equipment and other supplies
Overall, given the huge impact on some industry sectors, we were fortunate to have been in IT as our business was able to work remotely, largely without difficulty, and we were able to continue to support our customers’ needs.
M7: How do you prepare for an AI-centric world as a Business Leader?
CS: One of the fundamental technical requirements for good Artificial Intelligence is the availability of high quality comprehensive and timely data. For this reason, we are often asked to help, by using data virtualisation to abstract data from different sources in real-time and present the transformed results to AI models. Certainly, there is a huge buzz around AI and for about 10% of businesses, it’s already being used to give better faster business decision-making processes. Based on what we see there are 3 common strategies in the market, business-led, and technology-led and a combined iterative approach. The business-led strategy is driven by business demand for a specific capability. Initially, this seems to be largely for personalisation and automated customer service (chatbots), but now we’re also seeing a growing sophistication in use for real-time fraud detection/other “detection and prevention” models as well as improvements in operational efficiencies (e.g. customer onboarding)
Where we see technology-led strategies AI is being used by businesses using data science and business analytics to understand data and the potential for insights – availability, timeliness, quality, veracity & other attributes etc. What is where? Can it be used? What combinations have value? This approach is much more of a data-led initiative. The third strategy is the combination of business-led demand and technology-driven insights into what might be possible to solve certain business needs as an iterative process. In many companies, it is the work of the data scientist that shows the greatest potential for value in the use of AI/ML with new insights and new data combinations. At Denodo we see growing demand for data virtualisation as this substantially accelerates the access to data and therefore the surfacing of new meaningful insights that give a competitive edge to the business.
The value of AI may be compelling, for example for fraud prevention on payments, more advertising revenue from customised adverts but we have to balance this against the need for privacy compliance with GPDR, the Data Protection Act 2018, etc. The largest cost for non-compliance on privacy is often not the fine itself but the brand damage, so we often hear of priority being given to privacy compliance over analytics needs. There are also a host of ethical issues arising from the use of AI. In the short term enterprises have to show value to their own business and society. Many AI applications have obvious and direct benefits which most people would accept as reasonable but as the level of sophistication grows the issues will inevitably be more complex.