A consistent theme emerging from vendor online conferences this season is the democratization of data and what that offers to users in terms of their exploitation of the much wider availability of that resource. But there is a sub-plot to that process, and that is the democratization of the vendor community as a by-product.
This was the case at the recent SAS Institute Conference, where the subject of partnerships between vendors and complementary service providers subtly shifted from a ‘nice to have’ additional capability to ‘must have’ only real way to get to the next step. In the case of SAS, this was came in the form of a major enhancing of two existing partnerships, with Microsoft and KPMG.
In one way, the greater democratization of data and its exploitation means the de-skilling of the practice of working with data. But in so doing it brings to the forefront the capabilities of those that understand what they want to get from the data and what the answers might really portend in terms of business possibilities. To get to that point however, there has to be an equal absorption of current technologies into new enhancements that in one step both lower the entry point of the user interface, and consequently raise the opportunity level for new users to exploit what is now available.
Making vendors a seamless entity
Part of that process currently requires all vendors to acknowledge that achieving seamless collaboration with all complementary - and sometimes competing - vendors is a pre-requisite of creating pre-engineered, off-the-shelf service building blocks tht can be pulled together to create new data exploitation services in the democratized world.
This trend was highlighted by SAS co-founder and CEO, Dr Jim Goodnight in a post-event email exchange:
KPMG is among several strategic partners that we are working with to deliver solutions. KPMG is a long-time partner with SAS and has made a significant commitment in dollars and resources to Microsoft. It is only natural that the three companies would triangulate for success.
Goodnight was keen to point out that the drive towards data democratization did not mean de-skilling any part of data science. In fact, the ability for a wider range of individuals to produce powerful visualizations and analytics would enhance their ability to add real value to their own work:
We see it as a way to increase the size of the analytics talent pool in organizations and scale data-driven decision-making without having to dramatically increase the number of data scientists. By building capabilities such as best-practices templates, automated analysis and plain-language interpretability into our tools, we make it possible for users to do analyses that previously would have required advanced skills. This frees up data scientists to work on highly complex projects and serve as a best-practices team.
Indeed he stressed that one important aspect of data democratization would be the over-riding importance of maintaining rigorous data and analytic governance, as well as ensuring on-going staff training on best practices for analysis. He sees this as critical to ensure the right questions are asked, the right data is used, and that ultimately the work leads to usable results and better decisions.
The SAS/Microsoft relationship is a long-standing one that started life focusing on technology collaboration, but later moving into share go-to-market objectives. This latest revamp of the partnership takes that latter aspect to a new level. From the SAS point of view, Microsoft brings some straightforward, but valuable elements to the party: it still holds sway over the majority of user work environments, not just with Office, but all the server applications as well.
All of these are now core to Microsoft’s Azure cloud service. Combine this with the way the global pandemic has added significant impetus to the need for businesses to digitally transform their operations and become far more mutable – with the agility to change and adjust to changing market needs – and it stands out as one of the main platforms on which data democratization can be made to work. Goodnight told me:
Organizations around the world are moving to the cloud to innovate and move faster toward their business goals. As part of this transition, many customers are looking to migrate their SAS analytic workloads to Azure to improve performance and cost-efficiency. To provide a seamless experience and help organizations accelerate their cloud transformation initiatives, SAS and Microsoft are working together to ensure that SAS products and solutions can be successfully deployed and run effectively on Azure and the other Microsoft cloud solutions, including Microsoft 365, Dynamics 365 and the Power Platform.
Serving the needs of the citizen analyst
Candidates for the role of ‘citizen analyst’ can be found by the million amongst its existing user base, working with the many thousands of businesses that could readily exploit the democratized data if the right additional tools are available. That is what SAS is aiming at with its analytical tools, pre-engineered, business-focused packages and enhanced natural language user interface tools that will help break down the communications barriers between the ‘language’ of data science and its application/capabilities out in the real world.
This is where KPMG, another partner of long standing, could find itself playing an important role. The pair intend to establish a number of Cloud Acceleration Centers covering North America, Europe and the Asia Pacific region. It is certainly possible to see these as centers of data democratization, with the aim of helping SAS users to get up and running in the cloud as easily and quickly as possible.
KPMG and SAS will work together with those users to build multi-disciplinary teams that can focus on user specifics and how to get the best out of the technology in order to meet their business goals. The two have already developed a number of largely financial sector-oriented pre-packaged solutions. These will normally require tailoring to fit a client-specific requirement rather than full on engineering, allowing them to get into production with a solution more quickly.
With Christian Rast, KPMG’s Global Head of Technology and Knowledge, quoted as stating the new centers will enhance the current solutions portfolio, new additions to these pre-engineered business solutions, with a data democratic agenda, will soon start to appear.
A key SAS component in all this will be the new release of its core system, to be known as Viya 4. This will not be available until late 2020 and will feature what the company calls a ‘re-imagined’, cloud native architecture that has been specifically designed with the automation of data preparation, machine learning and model deployment improvements in mind. The aim is to improve productivity by widening the availability of analytical resources to those with other skillsets. It also makes extended use of APIs to make it easier for applications developers to collaborate with data science teams.
This is aimed at providing a flexible and efficient way to work with analytical workloads using an architecture designed specifically to work with containers and micro-services. It will allow users greater operational agility by decoupling analytics from the environments they run in, so that services can be scaled up quickly in response to demands from the workloads.
Tell me something meaningful
One of the key elements of Viya in its role as the platform on which data democratization can be built is the availability of a Natural Language Generation (NLG) user interface to the data and the analytics. Not only can it be optimized to allow users to pose questions using their own taxonomies and receive answers in that taxonomy, making the decision-making process far faster and more accurate, it will also add features such as automation through Auto-Text Analytics (AutoTA), and through the provision of natural language explanations of how it arrived at the recommendations it has made. This provides users with the information needed to optimise the analysis processes and make better decisions. According to Goodnight:
In addition to driving core business decisions, SAS's Natural Language capabilities also enable communicating the results of complex analytics back to the user in an easy-to-understand manner for richer insights. Examples include an automated explanation feature to provide key information about the factors that predict events such as subscribers churning, or why are customers providing high satisfaction scores.
NLG has been a part of the SAS environment for some time, largely in the form of Natural Language Studio. This has now been revamped and re-branded as SAS Conversation Designer and is planned to be released later this year to coincide with the release of Viya 4:
It will further strengthen the natural language capabilities of SAS Viya through a conversational interface, putting analytics in the hands of more users, and empowering customers to create their own conversational experiences.
In much the same way that the old-style typing pool was democratized by word processing systems, the time has definitely come for analytics to shake off its black art status and become an everyday tool of the business executive and anyone else with data-based decision making at the core of their work. It is therefore no surprise that SAS Institute, as one of the long-standing gorillas of the data analysis jungle, should be at the forefront of promoting the idea.
But it is interesting to see it also playing a part in what could be a major change in the way services are delivered to, and specified by, end users. Data democratization cannot be provided by one company – not for every possible user application at any rate. It will require partnerships of complementary vendors and service providers that can create the seamless collaborating services that function as a single entity for users. Indeed, it may not be long before organizations like KPMG become the effective brokers that form and manage such entities.