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What this blog covers:

  • Tech innovations happening every year in the data analytics industry.
  • A round-up on how GenAI started off as a liquid in 2023, quickly solidifying into a catalyst for turning the industry on its axle.
  • Trends, including price-performant querying, data federation architecture, synthetic data and augmented storytelling, that will become the new normal going forward.
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As we move toward 2024, one thing’s for sure – data analytics is still a hyper-dynamic industry that never stops advancing. Every year, a whole new slew of innovations in this space pushes the boundaries of human intelligence to flex enterprise-scale data with better, faster and deeper exploration.

Concepts like producing complex codes with a good ole’ prompt are no longer restricted to sci-fi movies or TV shows. Thanks to GenAI, they are much more plausible now for analytics too. Data profligacy is becoming a thing of the past with businesses now prioritizing price-performant querying.

Whether an organization is ready to tread uncharted territories in data analytics or is already there awaiting better avenues to explore, here are some key trends that I believe will make this journey easier, more informed and enriching.

1. Price-Performant Analytics Will Not Be a Trade-Off

Analytics as a business function is evolving and has embraced cloud technologies with much enthusiasm. Most platforms and tools allow businesses to expand the scope of their data analytics to the moon and back. However, when more users analyze the same data, cloud computing costs go through the roof.

Runtime processing for millions or billions of rows is not thrifty. Data offices are set to fail unless they create more right-sized data architectures, use AI to its fullest, and yet reduce analytical costs. This is where price-performant querying on the cloud can help. With this capability, organizations can weaponize teams to access and utilize the full depth and breadth of their data for making informed decisions instead of following a ‘gut feeling’.

To realize this possibility, data leaders will need to cut through the chase and find simplified analytics platforms offering enormous cost savings per user.

Kyvos helps control analytical costs with AI-powered smart aggregation. The platform processes data and feeds it into semantic models that can be accessed repeatedly without exploding costs. Another benefit is auto-scaling, which allows resource optimization by predicting load changes and pre-scheduling query operations.

In the world of modern technology tropes, we promise high-performance analytics at the lowest possible querying costs.

2. GenAI Will Move Further Inside the Wall

While maybe not as intuitive as the destiny-telling machine from “The Big Door Prize”, the rise of GenAI in analytics is nothing short of marvelous. Throughout 2023, it created a lot of noise, and for good reasons. In their sophomore and subsequent phases, LLMs or large language models (the kingpin behind ChatGPT) are reinventing machine- and human intelligence to change how we look at data.

Going forward, operationalizing GenAI will be non-negotiable for creating better customer experiences, streamlining redundant manual tasks, predicting future trends and uncovering gold hidden underneath massive datasets. Together, AI and ML offer advanced tools poised to achieve unthinkable feats, such as generating queries from simple natural language.

Who would have thought automation could simplify data processes and remove complexities from analytical workloads? Gartner’s research says that AI-augmented analytics will permeate 75% of organizations by 2024. Forget automation; it’s a complete flux.

I am talking about possibilities like:

  • Natural language querying (NLQ) to interact with a chatbot and seek answers from the full volume of enterprise data in English, getting responses in visual forms like charts or tables.
  • Natural language summarization (NLS) where dashboards deliver executive summaries of outliers directly to users and create opportunities for time savings and strategic focus.

All this is just the beginning. In the times ahead, data leaders will be able to use GenAI to put every data point to use. The only caution is being responsible in its implementation to stay on the right track.

3. Data Storytelling Will Gain Spotlight

Inventive data stories with dramedy-level charm are turning data workers into auteurs, signaling new paths for analytics. There’s no need to be a data veteran or expert coder either. All users can now have the power to turn data into insights with a few clicks, needing zero support from IT—that’s the new-age cultural shift.

However, the transition will hinge on the users’ ability to analyze massive and complex datasets without a glitch. One way to mend this is ensuring higher data literacy, a skill to understand, communicate and apply data in the right context. It’s a process of continuous training to bring people up to speed with the organization’s data maturity and readiness.

Data literacy will become a springboard to accelerate new analytics trends—speed, security and scalability being the impetus behind it. Analyzing raw data and turning it into compelling narratives can be simplified by unified, automated and customizable visualization tools. Instead of exploring only a subset of data, users can look at the complete picture with simplified and contextual visuals.

4. Data Federation Architectures Will Re(evolve)

Data federation isn’t a novel concept. But let’s admit this: enterprise data is often siloed, in diverse formats and stored in different locations. Bringing it into a common place for analytics can be the ultimate prize for promoting data-driven decisions. That’s why data federation will stay in the mainstream throughout 2024 as well.

Put simply, it’s a consumption layer over and above the existing data sources. Businesses don’t need extra storage or expensive infrastructure to make several full copies of their data. Instead, they can seamlessly connect to any source without building any complex data pipelines, ETL scripts or data lakes.

The architecture allows all the disparate datasets to work as a unified and virtualized unit. All this while data stays where it was—at its source— and still offers a unified and truly accurate business view. Integrating heterogeneous sources in real-time allows users to consolidate diverse nuggets of information and access it via federated queries.

Believe me, data federation is a must-have for modern organizations trying to steer ahead with full confidence in their data.

5. Synthetic Data Will Hit the Gas

Nothing defines synthetic data better than, “If you can fake that, you’ve got it made.” This faux or virtual data is created by computer simulations and statistically based on the original data sources using the same underlying mathematical properties. It can help fill gaps in or replace real-world data when the latter isn’t readily available or is too sensitive to be put under public radar.

Unlike a plain dead ringer, synthetic data helps organizations navigate tightening regulations, privacy laws, and information biases. Industry experts even foresee it to take over 60% of data consumed by analysts in the next 2-3 years.

The healthcare sector has already taken a quantum leap by using virtual data for accurate predictive modeling and disease forecasting. Others will soon follow suit, especially since protecting sensitive data is imperative in the new world order. When data is rare and costly, it can help citizen data scientists build and train advanced ML models for edge computing, robotics, embedded analytics, and a lot more.

The future may also see data lakes storing synthetic data for diverse use cases within enterprise data stacks.

6. Data Analytics Offices Will Become Proactive

Changing times call for new strategies. Actionable plans are needed to ensure a preemptive approach, instead of knee-jerk reactions to industry changes. At the end of the day, data analytics offices must drive paradigm shifts in enterprise data strategy. From governance to enablement for all, CDAOs (chief data analytics officers) have too many tasks with too few resources.

Data strategy is a team sport and building a strong organizational structure will drive many successful initiatives in the next year. From hiring the right talent to retaining the best data stewards and training them with the latest goings-on, the C-suite will need to reskill/upskill their teams and design executive actions to fulfill cross-department use cases.

Honorable Mentions

The way I see it, other significant data analytics trends to follow in 2024 will include moving further away from a data mess to streamlined data mesh architecture enriched with a Universal Semantic Layer, intelligent query engines, and fine-grained access controls.

Expenditure in edge computing will surge to $208 billion before we kick off the new year, making it integral to Industry 4.0. Research by HBR and Accenture also punctuates the rise of well-defined data contracts based on transparent agreements, trust and collaboration for owning, sharing and using data.

That’s a Wrap!

None of these initiatives will truly be successful without stringent data governance. When data engineers build products without putting solid guardrails for their protection, it just won’t end well. Adding more dollars to existing investments can’t work either.

Businesses need to augment data security and ensure regulatory compliance without missing a beat. Adaptive and flexible strategies will provide a safe route while promoting enterprise-wide self-serve analytics. With too many data transfers happening across platforms and applications, it’s a no-brainer. Kyvos’ multi-tiered security architecture covers all bases with strict access controls, row-/column-level security, data auditability and backups to cope with failures.

Looking Ahead

Though 2023 was a banner year for GenAI, 2024 will take this baton forward with modern data architectures, responsible data management and augmented analytical storytelling. Now, it’s time to invest in the most innovative analytics platforms that can help leverage the key trends and push organizations beyond the finish lines.

Kyvos can help extract tangible results with a modern platform built from the ground up for high-speed analytics, delivering sub-second responses on cloud-scale datasets without compromising performance, security and adaptability across departments.

To learn how we help companies redefine their data strategies and get maximum value from every resource they have, contact our experts.

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