Analytics as a service (AaaS) Model – what is it?

Data helps organizations isolate the causes of problems effectively and make informed decisions. Data assists organizations in allocating and structuring their resources. Data is vital! As organizations migrate to digital channels and scale up their online presence, they are generating more data than ever. Data is generated for every function — customer behaviour, product & assortment performance, buying behaviour, search trends, logistics, fulfilment, payments — and usually exists in different systems.

Real-World Challenges With Data

Given the plethora of data that is available to businesses, the ability to use this data effectively to drive business decisions is critical to success, especially in e-commerce. Organizations have, therefore, implemented multiple technology platforms such as data lakes, marketing automation software, and visualization tools. Yet, their real-world usage remains low.

Some of the primary reasons why this is the case include:

  1. Getting all the relevant data in one place is resource-intensive due to there being a large number of source systems, the varied and dynamic nature of data formats, and the frequency of data generation.
  2. Identifying and prioritizing data use-cases is a fragmented activity. This makes it difficult to create standardized data architectures on top of data lakes and leads to ad-hoc development.
  3. Businesses lacking the skills and time to create their own data views is also a chief problem. Hence, the bandwidth of the data team impedes timely access to desired data.
  4. Analytics getting equated with dashboarding is also a concern. Users get access to well-made dashboards, but finding actionable insights remains a challenge.

Analytics as a Service (AaaS) To The Rescue

 To combat these challenges, businesses need to leverage their data technology investments to deliver benefits. This can most efficiently be done by using Analytics as a Service (AaaS) Model. A model of this nature would focus on the following key areas:

  1. Cleaning data from different sources to preserve data integrity in centralized data lakes
  2. Maintaining data connections to ensure data accuracy and data consistency
  3. Creating standardized data architectures on data lakes
  4. Undertaking regular data maintenance tasks that require manual intervention
  5. Creating data use-case pipelines and implementing them efficiently to create a higher level of satisfaction among data consumers
  6. Focusing on creating actionable insights and presenting them to stakeholders in easy-to-consume data chunks

 

By using Analytics as a Service (AaaS) Model, organizations can better leverage their data technology investments. They can also improve the quality and speed of business decision-making.

 Iksula offers a complete Analytics as a Service offering for e-commerce businesses. Talk to one of our experts to know more about how we can help you leverage your data investments.

 

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