Data Analytics: Diversity, Distribution and Dynamics

The nature of data analytics is changing so fast that many businesses struggle to understand what tools and approaches they should consider.

Companies that fall behind are either failing to act or are focusing on the wrong issues. As a result, they are getting outdone by competitors, often digital natives, who understand how to make the most of modern methods.

Companies that succeed are doing so based on insights, agility, and the efficiency and the decision-making power that come from working with data they know they can trust.

Businesses that still rely on and try to manage the three Vs of big data — variety, velocity, and volume — operate from an outdated basis. What matters most now are the three Ds of data in the cloud: diversity, distribution, and dynamics.

The three Ds concern the different challenges of data, where that data comes from, and how to manage information that is changing moment to moment.

Other critical factors for achieving a modern data environment include treating artificial intelligence (AI) and machine learning (ML) as real concepts, not just theoretical ideas; and enabling data to go from anywhere to anywhere — for example, from ML models or cloud data platforms back into operational systems in a more enriched state — rather than following a linear path from source to analysis to dashboard.

Diverse organizations must grapple with the fact that data now comes in a wider variety of forms, including transactional records, research and development results, inventory reports, financial statements, customer care interactions, marketing efforts, social media, machine logs, and much more.

As mentioned above, we also have to focus on the influence of artificial intelligence on the modern data environment.

AI has always been a bit of an ethical quagmire. What exactly should AI be allowed to learn? Who gets to decide? Once it’s been activated, is anybody allowed to deactivate it? While AI is of course a part of the tech space, the difficulty some firms are facing is just how human, or superhuman, they really want to make AI. Equip an AI with enough learning capability, and who’s to say it won’t eventually become the dominant form of life on earth. Some scientists think that’s a distinct possibility. 

To counteract the slight existential angst that AI inevitably causes, Google says it’s developed a ‘big red button’ that could shut down any AI if necessary. But with several scientists warning that some AI could already be semi-conscious, who’s really to say that the computers haven’t already worked out a way around it. Makes you think, right? 

After I have scared you about AI and excessive ML, let’s go back to the main topic of data management and understand how important the three Ds of data are. The three Ds concern the different challenges of data, where that data comes from, and how to manage information that is changing moment to moment.

If you require more information on data analytics and data management, let me know. We have experts that can provide the tools processes for successful data science training. Please contact me at [email protected]