Are You in the (Big Data) 47 percent?

No, I am not referring to Mitt Romney's infamous fund-raising dinner comment about the percentage of people that pay no federal income tax.

I am talking about a recent report released by Dresner Advisory Services that says 53% of companies are adopting big data analytics.



My initial reaction was, well what are the 47% focused on if they don't have a big data strategy?
But then I realized the results line up with something that has become increasingly clear to me over the last two years to help companies extract value from their data: not all useful and valuable data has to be big data with a capital "B."



Top 10 Areas Strategic to Business Intelligence

The report reveals that the top 10 technologies and initiatives strategic to business intelligence are:

1. Reporting
2. Dashboards
3. Advanced visualization
4. End-user "self-service"
5. Data warehousing
6. Data discovery
7. Data mining, advanced algorithms, predictive
8. Data storytelling
9. Integration with operational processes
10. Mobile device support

Data Source: Dresner Advisory Services, 2017

Big data ranks number 20—and rightfully so. As data gets bigger, it also gets less meaningful and more complicated to manage and transform into useful insights. IT departments are the primary adopters of big data initiatives so far, but often the most ROI can be found in analyzing transactional business data like quotes, sales, shipments, returns, etc.

Biggest Adopters of Business Intelligence

Management and Operations tend to be the biggest adopters of Business Intelligence solutions due to their focus on improving operational efficiency and priority for growing revenues—and smaller data is usually most valuable for this type of analysis.

Advances in technology, such as machine learning, deep learning, and other artificial intelligence methods, are also creating new products and services that make it easier than ever for non-IT business units to get started with data projects that can immediately begin to impact the bottom line.

Looking at the above list, one can surmise that business users need an easy way to access their data to see what is happening in the past, present, and future, and to articulate that story to others to drive action and positive outcomes. Integration into existing processes, systems, and culture is also crucial.

What Will Change in 2018?

It is an exciting time to be working at the intersection of data analytics and artificial intelligence—as one feeds innovation of the other at a dizzying pace.

In 2018, we will hear more stories of companies extracting immense value from their data on an operational level, while technology advancements continue to drive down the cost and complexity of implementing these solutions that adoption rates will continue to increase even in mid-size and smaller companies.

Those that have already seen success in some departments or applications will continue to find new use cases and applications using data to streamline their internal operations and customer interactions.

There will be increased cooperation between big and mid-size enterprises and AI/ML/Data-focused startups, turbo-charging innovation, and ROI outcomes compared to the traditional enterprise + consulting + legacy tech provider models of early implementation.

So, if you do find yourself and your organization in the 47% of companies not yet focused on big data—don't fret! Look forward to all that you can do with your small data now, and by the time you've exhausted the value you can drive from it, there will undoubtedly be better tools, services, and apps to support your big data initiatives whenever they become a priority.

Just make sure you aren't sitting idle; having a data strategy is crucial to any organization in any industry today—big or small.

Nikki Hallgrimsdottir is a co-founder and chief evangelist of Algomus, where she works with manufacturers, distributors, and retailers to leverage artificial intelligence and data analytics. Algomus has built Algo—an AI agent that assists business users with data analytics and other related tasks through natural language processing, machine learning, and business process automation.

Prior to founding Algomus, Nikki worked for a decade with companies of all sizes to utilize automation technology in both manufacturing and product design. She knows enough to be dangerous in the areas of big data, analytics, artificial intelligence, IoT hardware, machine vision, supply chain, industrial automation, and multiphysics simulation, to name a few.

Opinions expressed by the author are not necessarily those of WITI.