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Top 5 Data Analytics Trends for 2022

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After decades of tinkering with data, the data analytics industry can finally see new transformations. The pandemic can be here to stay, but its worst will be behind us. And digital transformation will be accelerated. And the only fuel that can power this acceleration is data. New data analytics platforms, architectures, and technologies have opened new opportunities for businesses to be early adopters. 

Here are the top 5 trends in data analytics for 2022 and more importantly, what to do about them.


Table of contents:

1. The data analytics engineer replaces the data scientist as the world’s sexiest job

2. Data mesh, data fabric, and data lakehouse displace the data warehouse

3. Growing Interest In Self-Service Data Analytics

4. People analytics takes precedence

5. Data Integration Through Embedded Analytics

The data analytics engineer replaces the data scientist as the world’s sexiest job 

In many recent years, data scientist has been the sexiest job as more and more companies are committed to digital transformation. However, this role is gradually losing its appeal. There are many reasons for this. Companies failed to standardize new business models. Universities produced coders who lack knowledge in business contexts. And data scientists waste their days handling disparate data. All of that make companies lose interest in data science.

Nevertheless, the need to translate data into insights has become more urgent than ever. This has given rise to a new role: data analytics engineer. While data engineers only develop data pipelines for analysts or applications, data analytics engineers build data products that can both deliver data insights and streamline backend engineering processes. Data analytics engineers bring the best of both worlds. They have a deep knowledge of SQL like data engineers and at the same time understand the business domain no less than data scientists.

This growing need for data analytics engineers can be attributed to the growing use of cloud data platforms and data build tools (dbt). Legacy technical constructs such as cubes and monolithic data warehouses are being replaced by more flexible and scalable data models. Further, transformations on all types of data can be done within the cloud platform. ETL has been replaced by ELT. And who controls these transformations? It’s the data analytics engineer. 

First appeared in cloud natives and startups, data analytics engineers now can be found in large enterprises, such as JetBlue, American Tire, and Guy Carpenter. Now the demand for data scientists is about 2.6 to 2.7 times data analytics engineers, and this gap continues to close. 

Having data analytics engineers in place can be crucial to your success. In 2022, make sure to:

  • Adopt cloud migration as the newer design principle, which will require agile processes. 
  • Review skills in existing roles, and then have a plan to upskill and add data analytics engineers to your business units and functional areas. 
  • Employ new talents where necessary to build new data products and services at scale. 

Data mesh, data fabric, and data lakehouse displace the data warehouse

The data warehouse has been a tried and true concept since Bill Inmon’s first book “Building the Data Warehouse” in 1992 and after that, Ralph Kimball’s book “The Data Warehouse Toolkit”. Despite being valuable years ago, building a centralized on-premise data warehouse can take months. The costs for it could be intimidating.   

Coined in 2010, data lake promised to speed up data access to granular and reduce costs. Unfortunately, these so-called data lakes oftentimes became data swamps, too slow to deliver any value. 

The data lakehouse has the best of both a data warehouse and a data lake. It offers converged workloads for most of today’s data analytics use cases. Databricks uses this term in most of its material, whereas Snowflake instead uses the term Data Cloud. 

On the other hand, data fabric was coined by NetApp in 2014. Since then, it has become has evolved into a powerful architecture. Even Gartner advocates this data analytics trend. Data fabric focuses on metadata and AI to discover related data across cloud and on-premises data sets. 

Data mesh is both an architecture and concept pioneered by ThoughtWorks and defined in Zhamak Dehghani’s new book Data Mesh: Delivering Data-Driven Value at Scale. The end goal of data mesh is a data product. 

In 2012, data experts at Strata-Hadoop World claimed that the data lake would replace the data warehouse. That has yet to happen. However, newer concepts such as data mesh, data fabric, and data lakehouse will eventually replace the data warehouse. 

In 2022, remember to:

  • Stop thinking that there's a one-size-fits-all technology to adopt all these new concepts. 
  • Train and upskill data professionals in newer concepts while also assessing how ready the business is to adopt new methods and technologies.

Growing Interest In Self-Service Data Analytics

One problem that many businesses have is that many business stakeholders don’t have the skills or access level to analyze data on their own. The solution? Many businesses are investing in self-service data analytics solutions that require low to no coding skills while offering user-friendly dashboards, and various data visualizations.

In short, self-service data analytics can be defined as empowering users without technical knowledge of data analytics to access data and create their own reports and analyses.

However, although its definition is quite simple, self-service data analytics is often easier said than done.

To achieve a true self-service BI experience in 2022, there are two things to do:

  • Acquire a self-service data analytics tool that can help you achieve the goals you are after, both in the short and long term
  • Build a self-service culture where every user gets what they need when they need it

There are some critical capabilities that a self-service data analytics software should have, aside from the bare minimum such as data visualization, pivot tables, or dashboards:

  • Semantic layer — allowing users to communicate data queries across multiple datasets straightforwardly
  • Role-based access control — giving the right level of data management rights to the right people
  • Data governance — safeguarding your data and software from malicious attack
  • Customization support — enabling business users to analyze data the way they see fit
  • Prototyping support — allowing power users to clone existing artifacts or environments to prototype new capabilities

People analytics takes precedence

More often than not, people and HR analytics are usually the last on the list of a company’s priorities for data analytics, unless it's in professional services. For years, sales, supply chain, and marketing analytics have received more attention than people data.

But the pandemic has changed this forever. Organizations at every corner are struggling with employee shortages, worker safety issues, and unprecedented churn rates. Businesses that have higher visibility into their employees will win the talent war. 

But it’s not just about keeping employees. Making employees happier with higher well-being will eventually provide a better customer experience, products & services, and contribute positively to a business’ success.

Merck, thanks to people analytics, found out that the number of development opportunities is the most important factor to retain employees. Or Microsoft analyzed HR data to understand how remote working impacts team collaboration.

As talent will continue to make or break your business, resolve to these goals in 2022:

  • Extend people data to include data sources beyond talent management systems. Additional data sources can encompass employee engagement platforms and external data such as Glassdoor reviews.
  • Pay attention to data from Slack and Microsoft Teams to track engagement levels that can be the early predictors for attrition risk. Assess data on meetings to understand if there is a lack of cross-functional collaboration or inclusion.

Data Integration Through Embedded Analytics

Many users not only lack the skills to use data analytics tools, but they also don’t have the time or interface to integrate data analytics into their work. That’s why more companies are realizing the benefit of embedded data analytics, which makes dashboards and insights directly show up in the app that users are working with. 

By definition, embedded analytics is the integration of data analytics capabilities into applications, be it enterprise applications (e.g. CRM, ERP, EHR/EMR) or portals (e.g. intranets or extranets).

Embedding relevant data and analytics inside applications empower users to work smarter and more productively. Besides, embedded analytics makes data analysis accessible to non-technical users. 

For years, more than 26% of an organization has failed to adopt BI and analytics tools. Inside that 25%, most employees use the tools only once or twice a week. Embedded analytics can change that reality. Now that employees can insert charts, dashboards, and data analysis into other applications, their adoption of data analytics tools increases. With embedded analytics, most business users don’t even know they are using it - it’s just a part of their everyday work.

To effectively leverage embedded analytics in 2022, always:

  • Identify your analytics value proposition
  • Evaluate and augment your data sources
  • Think future-forward about your data strategy
  • Choose the right embedded analytics partner 

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