4 Emerging Data Modeling Trends for Tech Companies

For your business to remain competitive, you need to keep abreast of the emerging data modeling trends for tech companies. Your firm requires better automation, quicker data-driven decisions, and more integration. You also need more data, which calls for more optimized data modeling. Data modeling has been growing and changing to keep up with the increased data demands by businesses, especially tech firms. Here are four emerging data modeling trends to observe.

1. Just-in-time data modeling

Adapting to a rapidly changing world, there is a need to conceptualize and create fluid and faster data modeling to handle all your business data. Just-in-time data modeling increases efficiency by ensuring the production of those products needed at the market currently. Since your tech firm requires you to forecast demand accurately, this data modeling will come in handy.

Just-in-time data modeling entails the following:

  • Agile data modeling: This aspect of just-in-time data modeling uses a minimally good design with the right data model for specific situations relevant to just-in-time production.
  • Graph databases: Graphs provide easy-to-understand visual representations of business cases. They also offer an easy mechanism for evaluating group data and an understandable view of your business rules.
  • Business intelligence and reporting: this data modeling method allows the gathering of business information to determine the current market demand.
  • Wi-Fi 6: This technology is faster and portable for processing higher volumes of data from a wide range of systems — even the cloud.

2. Better automation and machine learning

Better automation gives you the leeway to train and create data models without too much technical knowledge. Under automation, virtually everything in your tech firm becomes mechanized. But it still requires human input in rephrasing any business challenges, handling complex data models, and validating automated data models.

When it comes to machine learning, augmented analytics continue to drive data modeling. You may need to invest in Knowledge Base Construction (KBC) and training in algorithms that make machine learning possible. Just remember that machine learning may flounder without targeted questions and quality integrated data sets.

You must also be sure that algorithms learning results from correct data sets. Algorithms from false data sets will lead to poor decision-making based on the wrong output generated by the computerized data models.

3. More widespread use of data modeling

As data model applications become mainstream, it is time to get on board. Already, several retailers, including major tech companies, are using data modeling to predict outcomes. You could start by investing in analytics software to analyze the traffic to your site and the conversion rates. Such data could help you design follow-up mechanisms that you can then automate for enhanced efficiency.

Data modeling is also important for people outside of statistics, data science, and analytics. These citizen scientists can use data modeling to generate predictive, prescriptive, and diagnostic data to propel their businesses to the next level. This is an additional clear indication that data modeling is becoming more widespread.

4. Focused data modeling

Focusing on data modeling means making metadata management a bigger priority. Your tech firm will benefit significantly from the complex enterprise knowledge capabilities developed in such businesses. For instance, your company could do well with better enterprise search capabilities, which will help you find more relevant data.

Data modeling has become more widespread in recent years and continues to grow. Tech firms can better adopt the latest trends to beat the competition and remain afloat in these challenging times. They should also evaluate the just-in-time, focused, and better automation and machine learning to enhance their data management.

Here at Helios, we have the experience and expertise needed to grow your tech firm through your data. If you need any help in data modeling fine-tuned for your tech company, contact us.



Submit a Comment

Your email address will not be published. Required fields are marked *