5 Trends Shaping the Future of Data Analytics and Insights | HackerNoon

The data analytics landscape is changing very quickly. For many businesses, the ability to gather, analyze, and interpret data effectively helps them to understand their customers, improve internal processes, and remain competitive. There are five trends that we think will shape up how businesses analyze and use data in the future. Let’s take a look at each of them and understand what they are.

1. Data Sources – Synthetic Data

Any kind of data analytics needs source data, and in today’s world this can come from nearly anywhere. Traditional data sources like customer databases, sales records, and website analytics are now joined by newer sources, such as social media feeds, IoT devices (e.g. drones), and third-party databases. But amongst all these newer sources, there is one in particular that we think is going to shape the future – Synthetic data. This is where Artificial Intelligence (‘AI’) is being used to create a simulation of data based on real-world information. While we are in the early stages of using synthetic data, it is becoming an increasingly important tool in areas where it is hard to source original data.

2. Data Infrastructure – Interoperability

With a growing number of data feeds and sources, it has been a constant challenge for many businesses to implement the right infrastructure. This includes both the software and hardware required to capture, store and process data across an organization. On the hardware front, one trend that has emerged over the past 10 years is cloud computing. This refers to the storage and processing of data in third-party data centres. Amazon was a first mover in this space with introduction of AWS. Nower days, the industry has matured with increased competition from other tech giants like Microsoft. Another trend within cloud computing has been to set up data lakes and warehouses. However, the trend that we are focused on is around software that can connect data from different sources in a standardized way – This is referred to as data interoperability. Having interoperability between different systems, such as data from the Customer Relationship Management system and the Enterprise Resource Planning system is critical for the flow of information. While many businesses struggle with interoperability, those that achieve it will have a real advantage by being able to leverage their data to gain deeper insights.

3. AI and Machine Learning (‘ML’) – Natural Language Processing (‘NLP’)

AI and ML shook the world in late 2022 with the release of ChatGPT 3.5, which was an information chatbot free for the public to use. Since then, AI and ML have become the most powerful tools in data analytics. This was initially focused on using structured data within companies to gain new insights and forecast with increased accuracy. The use of AI has moved at a very quick pace and now there are all sorts of new tools that can help organizations leverage their structured data better. However, the trend that we really think is worth keeping an eye on is NLP, which is using AI to analyze and provide insights from unstructured data. This includes extracting insights from data sources like video, images and audio, which has traditionally been a very time consuming exercise, until now.

4. Data Visualization – Storytelling

Data visualization historically refers to using graphics to present data. For most of us, these are the charts and graphs used in presentations and reports. However, we think that it’s becoming more about transforming data into a story that can be easily acted on. As the number of data sources increase, using data visualization as a tool to actually tell the story is becoming more important. Especially when it comes to informing people within an organization and having the right decisions made on time. Storytelling is a growing trend that simply combines data visualizations with the narrative. It makes it easier to explain the findings from complex data to a non-technical audience, and drive informed decision-making across all levels of the organization.

5. Talent – New Roles

The demand for data savvy talent is growing as analytics becomes more central to business strategy. From data scientists to analysts, organizations need skilled professionals who can make sense of the data and communicate it effectively. While there’s a push to make data skills more accessible across roles, there is also the creation of new data-related roles, such as “analytics translators”. This particular role is focused on bridging the gap between technical and non-technical teams. These professionals help communicate insights effectively, ensuring that business leaders can act on data insights without needing a strong technical background. This is one example of some of the new roles that are being created, in parallel with increasing demand for traditional data roles, such as data scientists and engineers.

Conclusion

The world of data analytics and insights is moving quickly. It is being driven by new sources of data, robust infrastructure, advanced AI and ML capabilities, improved visualization tools, and growing demand for data-savvy talent. While data analytics is typically focused on internal data, benchmarking is another tool that can uncover competitive trends and insights using external data. Organizations who stay on top of these emerging trends are well-positioned to continue winning into the future.