What is Analytics-as-a-Service?
Data Analytics is now an essential capability for every organisation and every sector – public and private, large, and small. But developing and operating a data analytics capability that efficiently delivers actionable insights has always been an expensive challenge. And one that rarely works the first time or is capable of quickly evolving to meet changing needs.
Just as Software-as-a-Service (SaaS) has become the dominant way to buy software, so Analytics-as-a-Service (AaaS) is rapidly becoming the way to acquire a modern data platform to support the entire Data Analysis Process.
AaaS solutions are subscription-based data analytics platforms and related services delivered through the cloud. They typically offer a fully customisable data platform and BI solution with end-to-end capabilities. Their role is to ingest, organise, analyse, and present data from multiple internal and external sources. And they enable all business users to gain insight and take action without owning the infrastructure and expertise overhead.
7 reasons to adopt Analytics-as-a-Service
- Firstly, it is an alternative for organisations that have outgrown their online ‘pay-by-credit-card’ solutions that can’t scale to meet their maturing analytical needs.
- Secondly, it is an economic alternative for organisations with more complex analytics needs but struggling to justify new investments in internal infrastructure and expertise.
- A third driver for AaaS is functions within an organisation that struggle to access centralised analytics expertise. It allows them to outsource their requirement and fund the costs from operational budgets.
- A fourth scenario is the need to consolidate data from multiple sources and create integrated enterprise-wide data sets and analytical capabilities beyond those that already exist.
- And the rise of Big Data is also a driver. The additional expense of parsing these massive datasets is leading CIOs to opt for AaaS as a cost-effective alternative to in-house solutions.
- But AaaS is also becoming a part of hybrid approaches. An organisation with a robust IT department and data scientists may use AaaS for only descriptive analytics. Or companies with less developed IT capabilities might use AaaS for more complex predictive and prescriptive analytics where they lack a platform or skills.
- And finally, by partnering with an AaaS managed services provider like CBIA, clients gain access to knowledge and best practice, models, and algorithms.
In short, AaaS is rapidly becoming an established part of a mix of solutions to the growing need for data analytics platforms and expertise.
CBIA Insights: A Modern Data Platform delivered as a Service
Mohan Bharaita of C-BIA in Pune started developing our CBIA Insights AaaS platform 3-years ago, initially with a focus on the data-rich and dynamic Finance and Retail sectors.
Both these sectors generate huge volumes of data from thousands of customer and product touchpoints. But companies in these sectors can struggle to develop and retain internal analytics expertise or consistently apply their data to improve performance.
By partnering with us using CBIA Insights, they have been able to extract more value from their data and demonstrably improve decision-making, something they struggled to do before.
CBIA Insights: Key Features
We’ll be publishing a separate post on CBIA Insights soon but the key features are:
- UK AWS cloud-based with on-premises option so fully GDPR compliant,
- Designed as self-service for all users – from power users to data novices,
- Minimal internal technical resource dependence, including for set-up and operations,
- Flexible to meet changing needs and take advantage of new data sources, types, and tools,
- Fast set-up and proof-of-value option before commitment,
- Subscription-based and with optional services and flexible to scale with need and usage, just like SaaS!
- These optional services include:
A DATASPRINT workshop to assess the business value of enhanced data analytics (download our DATASPRINT overview PDF here),
Data cleansing and ‘wrangling’ or re-formatting data, often time-consuming and labour intensive,
Exploratory data analysis to surface insights from new data sets and sources before committing to incorporate into the platform,
Dashboard creation and maintenance, whatever your preferred tool – Power BI, Tableau, Clik etc.
Advanced data analytics including AI and ML,
Knowledge Graph generation.
CBIA Databrooke: Scaling Up Retail Profitability
CBIA’s Databrooke is built on our Insights platform and is targeted at fashion retailers and their demanding analytic needs. The industry produces petabytes’ worth of data from thousands of touchpoints. This includes websites, mailing lists, in-store purchases, mobile POS, and more. This data must be constantly analysed to boost their revenue and profitability by staying focused on rapidly changing customer needs.
Maintaining in-house infrastructure and teams of data engineers is costly. Databrooke allows them to focus on what they do well – delivering fashion. They gain a richer understanding of their customers and improve decisions – optimising purchasing, distribution, and sales. And it means our unique Velocity of Sales (VoS) analytics is accessible to all staff without requiring a deep understanding of analytics, or the technology behind it.