KLASanalytics

KLAS analytics delivers end-to-end Data Science services to clients.

Our clients focus on the change that they need to make to realise the commercial value from data science, while we take care of data, technology, artificial intelligence, machine learning, integration of machine learning output with their systems, user experience and visualisation, security and privacy.

We have a simple subscription model that allows clients easy access to our intellectual property and data science skills.

What does Data Science as a Service do?

Our DSaaS covers many aspects, from producing strategic discovery and strategic analysis systems (see how we helped shape a crucial government policy here), to supporting key business processes, and creating, developing and managing data-driven products.

Our unique selling point is that we take the entire creative, technical, business partnering and integration burdens on in a full end-to-end package. In some cases, we also upskill and transfer knowledge to internal teams.

Our DSaaS service covers all the areas required to deliver data science as a scale, namely:

  1. Design: Business partnership, opportunity identification, solution/product management and governance
  2. Delivery: Technology provision and management and solution/product deployment
  3. Creation: Machine learning, insights, visualisation and product development

Our DSaaS engagement model

To deliver the service, we bring together technology, intellectual property and a well-balanced team. We focus on four types of capabilities:

  1. Technology capability: expertise in harnessing state-of-the-art cloud computing paradigms applied to machine learning for fast and iterative data product development.
  2. Data Science capabilities: data engineering, artificial intelligence, machine learning and visualisation experts with our own diagnosis and modelling intellectual property.
  3. Artificial intelligence, machine learning Business Partners (MLBP): experts in both management consulting and artificial intelligence, machine learning who interact with business and technology stakeholders to ensure that opportunities are well-identified, framed and delivered.
  4. Support team: we make sure that changes needed in business practices are understood and introduced. requiring a supportive team with programme and change leadership skills.

Our engagement model is simple. The MLBP is your in-house expert who advises your senior leadership team on strategic decisions related to your data strategy. They also ensure your technology and business teams are fully engaged, and co-ordinate the DSaaS team’s efforts, to do the right thing, in the right way, at the right time.

Our technical and support teams strive for full integration with your technology teams so that data, decisions and product output flow seamlessly.  Only full business and technology integration enables you to harness the opportunities that data science offers.

Nonetheless, our way of delivering value through DSaaS ensures that this integration causes minimal disruption because we provide you with all the technology investment and capabilities to introduce the necessary change.

Delivering value with DSaas

Innovation and collaboration

We deliver DSaaS in order to help clients benefit from the innovative power that the new data world can offer. We adopt a collaborative approach with your business and technology teams so that ideas are created, tested and validated. Our support team helps you embed new data products and processes to give you a core competitive edge.

In addition, we give you access to our partner network to ensure that you get maximum value from both technology and business perspectives. For instance, we provide governance frameworks as a key element of DSaaS.  We also partner with management consulting firms to provide the appropriate strategic governance that ensures that you achieve your targeted strategic goals.

Behind the scenes, we adopt an agile approach to everything we do. From creating ideas and algorithms, our team’s approach client situations by splitting them into small problem statements and tackling them efficiently and in parallel.  Dead ends are discovered sooner, minimising time to market and maximising the chance of success.

Value Centricity

Our promise is to turn data into a competitive advantage. Therefore, value is central to KLASanalytics DSaaS.

Depending on the business, the essence is solving complex problems in the best possible way. For instance, when delivering strategic data science, the key value is to allow clients to make the right decision at the right time.

Initially, we assess the following attributes:

  1. How fast does the decision need to be made?
  2. How accurate does the impact assessment need to be?
  3. How are they delivered to the decision-maker?

The answers to the above questions help us invest in the right way:

  • Is it technology that generates near-real time decisions?
  • Is it a data gathering and curation that give access to unique insights and enable the consideration of issues such as sustainability in the long term?
  • Is it sophisticated algorithms that reduce uncertainty?

What matters is what is needed to achieve our promise. Our governance processes and ways of working help you to remain value-focused.

Velocity in execution

We are big cloud fans, but not just any cloud. We made a strategic choice to build our technology stack and intellectual property on new computing paradigms.

For us, cloud means three key business values:

  1. Achieve our goals as fast as possible through the ability to work iteratively, deploy new ideas, test them and validate them fast.
  2. Reduce maintenance overheads and focus on innovation and value creation.
  3. Transparency and flexibility in commercial arrangements.

Therefore, we invested in new paradigms such as server-less technology, natively scalable technology (computers dynamically manage how big our computing power needs to be), and NoDevOps application development (we launch applications, update them and test different configurations with no need to manage backend systems or go through complex route to production steps).

The result is ‘Manjam’, our big data and artificial intelligence, machine learning platform. It has all the features that the big data vendors advertise, but with added flexibility and minimal operational overheads.

It’s core focus is scale. For us, scale is defined by two dimensions:

  1. More applications: we create artificial intelligence and machine learning products that transform your business by applying the product at a large number of use cases. Our way of thinking is solve once, apply many times.
  2. More data: regardless of the volume, velocity or variety of data, Manjam can scale and create value.

Under the hood, we have created a kubernetes based system that allows for multiple instantiations of a product, while our artificial intelligence, machine learning continuously develop and improve the product.

Manjam automates the onerous process of moving artificial intelligence, machine learning applications from development to production. It takes care of everything including microservice creation, model management and user experience.

It also has access to technologies which are only available to those who choose new paradigms (such as Google’s BigQuery, BigTable, Natural Language API and Knowledge Graph).

All this allows us to provide our clients with a truly cutting-edge service in a faster and more efficient fashion than traditional consultancies, especially when combined with our agile ways of working.

State-of-the-art technology

Big data technology is all the craze. However, like all technologies, it requires investment, management and operating. The overheads can be substantial, especially if iterative testing and agile approaches are to be adopted.

Several organisations try to minimise the cost by adopting cloud technology: the provision of platforms as a service. Despite its usefulness, we’re not satisfied. We want easier and faster approaches.

Thankfully, cutting-edge cloud computing goes a long way to reducing the burden.  That is why we made the strategic decision to adopt Software as a Service (SaaS) and specifically, the concept of server-less development, the logical conclusion of which is the NoDevOps paradigm. Our DSaaS team manages data, builds and deploys solutions on a cloud technology that doesn’t require the spinning of VMs or clusters of VMs whenever possible. We load data into completely distributed, scalable data management tools that are always on, giving us more time to be innovative, reduce costs and accelerate the speed at which we can deploy our outputs.

We partnered with Google Cloud Platform because of their long-term technology vision. Why would we bother with complex clusters if we can load data to BigQuery?  Why would we run complex DevOps processes and infrastructure management for our applications if we can immediately deploy a new version using AppEngine in one simple line of code?

Business, ML, data management and technology excellence uniquely coalesce with our DSaaS offering.