Building a successful data analytics practice

During 2016, we recognised that an increasing number of our client engagements centred around data analytics software, while our market interactions pointed to a growing need for support in the space.

So we decided to establish a dedicated Analytics Practice within Sonalake to consolidate and further develop our expertise in the area. The decision proved to be very beneficial, and a nice recent milestone was that we were honoured to win this year’s Analytics Institute Fellowship & Industry Award in the emerging technology category, recognising the impact of our work in transforming data and visual analytics in the telecoms vertical.

From just 8 people to 40+ specialists, this post shares our journey in developing a dedicated Analytics Practice Team over the course of 3 exciting years.


Strategic focus - the solution:market fit

The initial challenges facing the team were both strategic and organisational in nature.

Our activities in the data analytics space covered a significant breadth, with varying depths, so the first initiative was to carry out a strategic business analysis to define and categorise which capabilities and solutions to focus on and develop.

A lean business model canvas approach was employed to refine the ‘solution:market fit’ and set the basis for the technical and market development of each selected solution area. This was an iterative and extensive process, and in reality, it never really ends as we regularly revisit it.

The result of this evolution has been an Analytics Practice that supports the following primary solution categories:

Data Platforms Building modern, highly-scalable open source platforms for the efficient gathering, storage and streaming of data to enable analytics applications.

Analytics Applications Custom development of applications that cannot be fulfilled by available COTS products, including embedding analytics modules within vendors’ software products.

Data Visualisation Advisory services on designing the user presentation of analytics outputs - primarily dashboarding and reporting, based upon expertise in user experience (UX) and the available tooling, including commercial BI and open source software products.

Data Science Advisory and development services for advanced analytics features based upon contemporary machine learning and data science capabilities.


The team’s target market and customer segmentation model evolved to differentiate between Enterprise Clients and Software Vendor clients and between different vertical industries.

  • Enterprise clients are focused on understanding the value that Analytics software will bring to their business. They seek to analyse how their underlying data can be translated to provide insights and democratised to provide access across user groups in the most relevant context.
  • Software vendor clients are mostly focused on the technology selection and development challenge in selecting the right commercial or open-source technologies to embed within their products, thus offering analytics features back to their own customers.


Innovation & skills - building the organisation

The team’s capabilities then needed to evolve and be structured into an organisation to support the strategic solutions. These skills included:

  • business analysis
  • UX design
  • data science
  • software engineering
  • pre-sales consulting
  • innovation management

It was important for the Data Analytics Practice team to act effectively as an end-to-end business, however, it was also essential that it would share many functions and resources with the wider Sonalake organisation. An organisation structure was developed with dedicated management and technical resources to achieve the focus required, while also utilising shared resources - for example in software engineering - to efficiently manage demand and growth opportunities.

The organisation model was designed as follows:

  • A flat management model, with minimum hierarchy and no silos
  • A network of project teams to manage the volume & diversity of work (application development, analysis & research, consulting projects...)
  • Central supports for the development of People, Process and Technology
  • A high degree of fluent and efficient communication and rapid information flow between projects and central supports

The organisation model was supported at the outset by the established team culture within Sonalake and has strongly fed into the evolution and refinement of this culture over time.

The team recognised early on that it did not have all of the skills and capacity within to evolve as per its strategic plan and so partnering became an important element. A key initiative in this regard has been our engagement with third-level research institutions specialising in the sector.

A good example has been our work with CeADAR (Centre for Applied Data Analytics Research) within UCD & DIT, with the following highlight projects:

  • Development of research prototype demonstrators in the areas of Time Series Clustering and NLP.
  • Hosting of post-graduate Data Science specialist students for joint research programmes.
  • A successful bid for DTIF Funding (in collaboration with Exertis Supply Chain Services) for a major 3-year innovation R&D project for ‘Blockchain in the Technology Supply Chain’ incorporating an advanced data analytics solution within the shared governance blockchain model.


Customer engagement - delivering for success

From a customer engagement perspective, while sharing many common facets, almost every requirement for data analytics and intelligence is unique. The team’s ultimate success is dependent on achieving a level of collaboration and transparency with the customer that facilitates an aligned understanding of the target need and solution.

We don't jump into developing solutions, instead, we start Co-Creating with a Design Phase: focusing on the intersection of User needs, Business goals and Technology constraints & opportunities to agree on the scope of the solution and the ultimate user experience.

The team uses its experience to guide the business analyses to the source and potential of the most valuable data, use cases and user journeys, mapping out the requirement to inform the technical solution.

With the design shaped to a sufficiently detailed extent, the team’s ongoing research of open source and commercial software allows it to select the best tools for the job, spanning technology, operational and commercial inputs.

The team has experienced lots of successful customer projects across market verticals. Good examples include a major Data Analytics Enterprise Platform for Travelport, and a Data Visualisation and Executive Dashboarding solution that has been adopted by several large brands in the telecommunications vertical.

We look forward to the ongoing development of the team in such an exciting space, and we feel privileged to get to evaluate and apply advanced data science, machine learning, user experience design and computer science techniques to real-life enterprise challenges and opportunities.


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