I’m overenthusiastic about the end-of-the-year wrap-ups and predictions. It’s the perfect time to zoom out from the daily grinding and see the big picture. I love reading articles on learnings and trends from my peers as well as industry leaders, taking time to review my year, and planning for the next one. So I wanted to share my take on what will be top of mind in data analytics next year.

2022 hasn’t been the easiest year for most. Due to the recession, we witnessed a high number of layoffs and budget cuts. “Doing more with less” became a phrase that we all used more and more. And I expect it to be the motto of 2023.

So, I predict an increased focus on efficiency, business value, and maximizing ROI for analytics teams. As a result, only projects and technologies that deliver the following will be prioritized:

• help teams save time and resources, increasing efficiency
• help teams save revenue and cut costs, maximizing data & analytics ROI Here are my predictions for some of the most important analytics trends heading into next year (in no particular order).

 

Prediction #1 — focus on proving the ROI of analytics

Data-forward companies made significant investments in technology and people over the last years. Initially, their focus has been on collecting, storing, managing, transforming, and displaying data to establish a strong core.

Data quality is essential to creating meaningful results, but it isn’t enough to create business value. If we were to see the data analytics journey as a marathon and the business impact as the medal, delivering actionable insights that inform daily and strategic decisions are a must-have to complete the race. Hence this is where the leaders are shifting their focus to maximize the ROI.

So how can you get there?

• Closer collaboration with the business teams (e.g., nailing priority use cases together, and daily/weekly performance reviews to review and share insights)
• Speed and comprehensiveness in analysis to truly empower decision-making
• A strong focus on measuring business value and continuous iterations to achieve the best results

 

Prediction #2 — most decisions will be augmented with ML

To be able to keep up with the pace of data and business, the “doing more with less” motto surfaces once again. Businesses need to augment workflows and automate menial tasks, accelerate speed to insights and break out of speed and comprehensiveness trade-off to create true business value and maximize the ROI of data analytics.

Decisions that use data can be automated in a variety of ways and fall somewhere between being mostly human-based and entirely automated. Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated. In the age of artificial intelligence, organizations that embrace some degree of decision automation are likely to achieve competitive advantage through more rapid, sophisticated, and granular decision-making.

In BI, most decisions are going to be augmented soon. Two main use cases I see emerging in most data-forward companies are:

• Human-in-the-loop diagnostic analytics: using ML to test every hypothesis in the data and score the drivers of key business metrics changes. Humans can focus on the last mile of filtering out the most relevant ones and making decisions
• Automated alerts on key business metrics changes that are delivered via Slack/Teams in natural language

 

Prediction #3 — data & analytics roles will further specialize

As professions start to mature and become more complex, further specialization often takes place. This has been a natural evolution and analytics won’t be an exception.

Currently, data & analytics team roles are segmented primarily based on the stages they own in the data analytics workflows

• Data engineers pipe the data in
• Analytical engineers clean the data
• Data analysts and scientists draw insights from the data

These core roles will stay, but there will be further segmentation focused on specific business problems and goals.

• The analytics engineer role will continue to grow, ensuring data modeling and delivering clean datasets to data analysts and business users. This trend also frees up data analysts.
• Data analysts will become closer to the business, focusing solely on uncovering insights and improving decision-making to become trusted advisors and maximize business impact
• The data product manager role will continue to grow, boosting the adoption and monetization of data products
• The analytics translator role will continue to grow as a way to close the data literacy gap and maximize the value of analytics use cases

 

Prediction #4 — data mesh for data democratization and scalability

Data mesh has been the hype word of the data talk for some months. But what is triggering this trend? The desire for democratization and scalability of the data architecture.

Many organizations are still struggling since their architecture choices were driven by technology instead of business needs. Data mesh aims to solve this problem by federating data ownership within the organization and focusing on efficiency, innovation, and transparency.

It creates a distributed, decentralized way of dealing with data, making it faster to share and create data products. Data products are owned by independent cross-functional teams but abide by central governance to ensure interoperability and consistency.

Breaking down silos, improving the reuse of data, and fueling innovation, data mesh and business problem-oriented architecture will be the way to go for companies that want to do more with their data.

 

Prediction #5 — analytics teams define speed to actionable insight as a north-star metric

Most of the data-driven answers take days or even weeks. As companies now have data available in near real-time, decision latency is the current bottleneck in analytics. As businesses change faster than ever, these answers are not coming in at the speed of business. This reactive approach has significant costs: direct negative business impact, undermined data culture, and a downward spiral leading to attrition.

Improving this situation is becoming a top priority for many analytics teams. The top analytics teams have defined speed to actionable insight as their north star metric to ensure actionability, maximize business impact and elevate their data culture.

Speed to actionable insight is a simple concept: the time from a business question to a data-driven decision or action. It already includes the multiple iterations that are often required. This way analytics teams not only focus on answering questions but rather proactively work with the business to drive actionable insights.

This is driving innovations like augmented business root-cause analysis and automatic alerts and insights messages powered by ML. This ensures that teams leverage all the available data, proactively delivering analytics at the speed of business.

 

Prediction #6 — Data regulations will become more top of mind

Regulations such as European GDPR, Canadian PIPEDA, and Chinese PIPL pushed companies to rethink the way they collect and work with data. I - and also analyst houses such as Gartner- expect to see more countries introducing new rules and legislations in 2023.

So it means that there is more work to be done to win the trust of customers so that they will be more willing to share their information. To achieve this, businesses need to better structure, record, and create more transparency on how they process and handle data. Moreover, effective data governance will become more relevant. Depending on the compliance stage, this could mean some retrospective work on the data at hand, how it has been collected and stored and how it has been used.

Perhaps not the most exciting prediction on the list but a critical one in maximizing the value of data, adhering to security standards, and preventing any violations and fines.

 

Conclusion

Teams keeping a strong focus on ROI and not letting any resource or minute go to waste will be the winners. I hope to see analytics teams working closely with the business and focusing on data analysis, insights generation, and taking action to maximize the business value. I expect data foundational projects to be deprioritized unless they are critical or bring significant cost savings.