self service analytics

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Self-service analytics are getting popular. Here’s why

With a global shortage of skilled tech workers affecting organizations, self-service analytics are becoming a new norm for businesses looking to leverage their data without being dependent on their IT teams.

A form of business intelligence, self-service analytics empowers business professionals to perform queries and generate analytical reports with minimal IT support.

In the Asia Pacific, data-driven businesses rely heavily on insights from their data. While there are many automated data analytics solutions in the market today, understanding the data can be challenging if the company does not have sufficient IT personnel.

Managed service providers can provide the desired insights, but with data privacy concerns, companies may not be able to let MSPs have access to most data.

Today, employees can be empowered to access all the data they need, wherever and whenever they are with the right self-service analytics system. However, despite all the buzz surrounding self-service analytics, the process of implementing a sustainable system has been far from successful.

In 2018, Gartner reported that organizations are embracing self-service analytics and business intelligence to bring these capabilities to business users of all levels. This trend is so pronounced that Gartner predicts that by 2019, the analytics output of business users with self-service capabilities will surpass that of professional data scientists.

Fast forward to today, self-service analytics seems to have surpassed professional data scientists with more organizations looking to implement it. With the shortage of professional data scientists and skilled IT employees bundled together with the effects of remote working, there is no denying that more organizations are gunning for this option.

Convoluted data architectures, inefficient processes, and a lack of data governance to ensure that companies are even storing the right information in the first place continue to hinder such systems from becoming a reality.

In fact, many companies – including the largest and most tech-savvy in the world – struggle to operationalize their analytics across distributed computing environments, or to meaningfully leverage ever-increasing volumes of data coming from AI, machine learning, 5G, and IoT.


 Is self-service analytics secure?

According to Keith Budge, Executive Vice President, Asia Pacific and Japan at Teradata the quality of the data requires a lot of care under data governance and security, especially when industries like banks and government agencies are serving customers remotely.

When it comes to data for self-service analytics, organizations need to ensure their employees have accurate data that is up to date and highly secured, especially in a remote work environment.

“In regulated industries like banking, data governance and data security have to be assured for self-service analytics at much higher levels. During the pandemic, data governance and data security became paramount, especially with employees now working remotely and using their own devices for work as well,” said Budge.

For example, Budge explained when a bank implements a new self-service analytic tool, a lot of testing is done to validate the security and veracity of the data. Depending on local law and regulations, some banks and companies even have to demonstrate to regulators the measures taken to ensure data that is used for self-service analytics is not compromised.

Budge added that this is where companies like Teradata can bring together data from very diverse sources and complexity and help customers be compliant with both their internal and regulator security requirements. This is highly crucial in regulated industries like banking, especially with risk management issues being fundamental to the ways banks operate.

Teradata Vantage is the connected multi-cloud data platform for enterprise analytics. It enables ecosystem simplification by unifying analytics, data lakes, and data warehouses. With Vantage, enterprise-scale companies can eliminate silos and cost-effectively query all their data, all the time, regardless of where the data resides – in the cloud using low-cost object stores, on multiple clouds, on-premises, or any combination thereof – to get a complete view of their business.

At the same time, Budge pointed out that the industries that are on the less regulated end of the spectrum are some of the new eCommerce startups that have been able to get ahead of the regulated industries. These internet and cloud-intensive businesses depend very heavily on data. They can do a lot of things much faster, and don’t need specific regulatory clearance on what they do.

“However, with some of them moving to “near banking type” work, we are seeing them becoming under the purview of the regulators and being subjected to same regulations as banks,” explained Budge.

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Solving the skills shortage problem

Whilst self-service analytics can help businesses rely less on IT teams for analytics, managing these tools still requires some training, be it for large enterprises or small and medium enterprises (SME).

Larger enterprises often have sufficient capabilities to train their employees on self-service analytics. For example, a contact center agent is expected to understand all the data that comes to them. As such, they’d rely heavily on their data science and analyst teams to build their self-service use cases and applications for their employees.

“People behind the scenes at that company do all the hard work to make it easy for employees using the end product. Unfortunately, SMEs do not have this luxury. They have limited capacity and resources to develop self-service applications,” explained Budge.

To deal with this, Teradata is working with its local partners around the world to build self-service applications that SMEs can use. Leveraging the Teradata platform and capability, local partners are building self-service applications on top of Teradata for the SME market.

“We enable other parties to build, deploy and manage self-service applications into the medium or SME markets,” added Budge.

With AI also being a key component in analytics, Budge believes that AI models in self-service analytics will be able to self-learn predictably at scale and be deployed over user environments.

He believes there will be little AI models sitting everywhere in the future, and this will only make data analytic tools ever so more important.

Self-service analytic tools may not just help organizations rely less on their IT teams but also ensure that businesses can remain competitive in the data-driven market. With the right system and training in place, employees will be able to make the most out of self-service analytical tools to maximize efficiency.