Demand for data analysts is skyrocketing. In fact, in 2020 alone, nearly 100% of companies had their sights set on bringing on more data analysts than ever before. This spike has translated into record-breaking salaries and tremendous competition for those trying to break into the field, and those working their way to the top of it. As a result, expectations for the role and the skills required have changed dramatically.
New Skills Required for Data Analysts
Years ago, you could find a good job as a data analyst as long as you knew your way around Excel. Over time, an Excel skill set became table stakes, and proficiency with BI tools like Tableau and Power BI were what differentiated great analysts from good ones.
Times are changing once again. Even a background in multiple BI tools is insufficient for helping you stand out. As businesses look to become more sophisticated with how they utilize their data, hiring managers have had to level up their expectations to include:
- Competency in SQL and Python
- Mastery of data cleansing
- Comprehensive understanding of data modeling
- High level of familiarity with statistical concepts and algorithms
Both businesses and job seekers need to recognize that the role of a data analyst has changed significantly.
Hiring Data Analysts
These expectations have put tremendous pressure on businesses. How companies utilize their analytics is changing much too quickly for many to wait around for some ideal candidate. In response, many organizations have adopted what Gartner calls the “popularization of data analysis.”
What this means is that in order to keep up with the rapid pace of digital transformation, companies are taking a citizen data analyst approach to their analytics workforce. They are focusing on developing the right skill sets in-house to turn every information worker into an empowered data analyst that is needed to inform business decisions now and in the future.
Any business that has taken this approach understands that it’s easier said than done. So, what can you do to ensure your organization’s analysts are trained and equipped to embrace a digital transformation?
The good news is that you’re probably 50% of the way toward a solution already thanks to your current BI investments. To get the rest of the way there, you can rely on one of the most popular BI tools on the market today: Tableau.
Tableau for Citizen Data Analysts
Tableau, like other similar BI tools (Power BI, MicroStrategy, etc.), provides users with an easy to navigate interface along with all the integrations and capabilities they could need to transform data into insight. In fact, today’s BI tools do a lot of heavy lifting in the effort to transform information workers into citizen data analysts.
But the primary challenges businesses will face with digital transformation is not how they will furnish the tools their employees need to analyze the data. Instead, businesses will discover that the true challenge is in developing a robust, flexible, and fast data architecture that ensures all of this analysis can take place at the speed of business.
In a recent webinar, we shared tips on how companies can best support query analysis with Tableau as they scale analytics operations across the business. It’s worth watching the whole session, but let’s take a look at the key points below.
Architecture Essentials for the Broader Adoption of Data Analytics
When it comes to democratizing analytics across your organization, automation is the name of the game; augmented analytics to be more specific. What this means is that AI and machine learning (ML) will be the critical bridge between your data, your BI tools, and your analysts. AI/ML and automation will increasingly act as an accelerator and multiplier for your analytics work and can help refine the skill sets modern data analytics work requires.
While augmented analytics comes in many forms, there are several key automation-related capabilities it enables that you should be looking out for when modernizing your analytics infrastructure.
A Unified Semantic Layer – At the heart of all of these popularization efforts is the need to enable users from across the company to be able to formulate insights from all relevant data sources, not just the few their department has access to. A unified semantic layer makes it possible for users to pull data from previously disconnected sources with minimum effort, providing a clearer picture of what’s going on with the business and delivering more reliable insights.
Self-Service Analytics is arguably one of the most important capabilities when designing any system for citizen data analysts. Not only does a self-service analytics architecture accelerate insight generation, it’s more economical too. Integrated with BI tools like Tableau, self-service analytics means analysts can pull the data they need when they need it without having to wait on a data engineer.
AI-Automated Modeling – Data modeling is time-consuming work that can require skills that not all of your analysts may possess. Fortunately, this is an area where modern advancements in AI shine. Investing in solutions that handle this repetitive work dramatically reduces the cycle time of your data analytics work.
Unified Permission Management – The leading concern of any CIO or CTO when you mention citizen data analytics is security. As you expand access to your organization’s data across departments, it’s vital that you have a system in place that can granularly scale your security along with your analytics.
There’s never been a better time to consider augmented analytics. New solutions and technologies abound that provide some or all of these features and much more. One example is Kyligence Cloud, which covers all of the points above, all the way from data to insight, and easily integrates with all major BI tools like Tableau.
Let’s take a look at how all of this comes together with an example.
Case Study: Self-Service Analytics for Financial Services
A major bank wants to leverage multi-dimensional analysis to run calculations across all of their bank card transactions. The main dimensions and metrics include dates (year, month, week, and day), card attributes, brand, region, institution, number of transactions, and transaction amount, to name a few.
Due to the limitations of their data warehouse, the bank had to build separate data models for each business entity and provide that data through SQL retrieval. This contributed to three primary challenges.
Pre-Augmented Analytics Pain Points
- Long Lead Times for Analysis: Analysts are highly dependent on IT to help connect data and support queries leading to long waiting times for any analysis.
- Poor Data Quality: Large deviations in analysis result from inconsistent data standards being applied to the backend.
- Limited Scalability: Very difficult and time-consuming to share insights across the organization and publish reports.
Deploying Kyligence, the bank was able to build a unified data model that could be synchronized with Tableau. This greatly reduced lead times, eliminated previous data inconsistencies, and allowed the analysis of multiple reports with a single model.
Today, the bank has over 5,000 monthly active users, with the total number of queries per month exceeding 1 million. More than 90% of those queries respond within 1 second.
Get More from Your Investment in Tableau
As expectations for data analysts rise to meet the needs of today’s businesses, our BI tools and data architectures will need to evolve as well. AI-augmented analytics are rapidly maturing into an indispensable technology to help ease that evolution and establish new work patterns for citizen analysts.
Fortunately, with the right technology, this doesn’t need to be a time-consuming, costly, and complicated process. In fact, you can start experimenting with solutions right now for free with a cloud-hosted trial of Kyligence. You can take it for a test drive here.