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As business leaders, we are constantly looking for new and improved ways to satisfy and grow our customer base, attract and retain excellent employees, and develop and produce products and services that anticipate and address always-evolving consumer needs. In short, we all want to be more effective data-driven businesses, because better data and better use of data together lead to better business outcomes.
The evidence bears this out: in a study conducted by BMC and 451 Research about how businesses characterize their progress in their evolution to an Autonomous Digital Enterprise (ADE), overwhelmingly, the number-one tech priority for the majority of the more than 1,200 companies surveyed was to become a data-driven business. These businesses are striving to use artificial intelligence (AI) and analytics to extract and monetize valuable data from both traditional and new sources like the Internet of Things (IoT) devices, social media, and customer engagement systems. And these companies are putting money behind their motives: worldwide spending on AI solutions is expected to reach over $342B in 2021, and spending on big data and business analytics (BDA) solutions is forecast to reach $215.7B, an increase of 10 percent over 2020.
Data is also driving tremendous advances in the public sector; for example, India’s Smart Cities Initiative is harnessing data and digital technology to help address the most pressing issues in infrastructure development, creating opportunities to make sustainable and inclusive decisions that improve the quality of life.
So where does all this data come from? The better question is, where doesn’t it come from? With trillions of data points gathered from consumer activity and enterprise systems, mobile devices, IoT/connected devices, social shares, science and engineering, and so much more, virtually everything is a data source, and the numbers only continue to expand. In fact, a recent report on the data and information created, captured, copied, and consumed worldwide shows that data volume in 2020 reached an all-time high at 64 zettabytes, and by 2025, that volume is estimated to almost triple to 181 zettabytes!
But how can organizations today capitalize on this explosion of data in their efforts to evolve to an ADE? Luckily our knowledge and abilities around proper data collection, storage, processing, and analytics are also expanding and improving, fueled by rapidly evolving AI and machine learning (ML) capabilities. Data and analytics transformation projects are built on solid approaches like compelling business use cases, foundational and innovative architectures for value generation, updated organizational structures and shared-service capabilities, and an enterprise-wide culture of embedding data and analytics in organizational processes and behaviors.
Yet even with all that planning, the majority of data and analytics transformations aren’t viewed as successful, with most companies only analyzing a small percentage of the data they own, and the majority failing even to deploy to production.
The reasons for these failures are many and complicated. Here is a snapshot of the most common stumbling blocks:
- Rising, expanding, and novel data sources generated by complex business models are difficult to simplify and extrapolate
- Existing data management processes and practices don’t work well with newer technologies
- Failed collaboration and/or inadequate business involvement
- Insufficient investment for robust data analytics programs
- Scarce talent pool or inadequate/incomplete staffing
- Inability to operationalize at scale to meet stakeholder expectations
- Too much focus on new tech versus value delivery and organizational readiness
- Incomplete or unclear approach to defining milestones and success
We know now how much data we are generating, and we understand the necessity of operationalizing that data for both public and private interests, allowing us to drive the kinds of business outcomes that improve lives and ensure a competitive edge, as well as the stamina to withstand ongoing disruption. So how can we ensure that we’ve prepared our organizations, our tools and processes, and our data adequately to make our data and analytics transformations successful? At BMC, we believe the answer is DataOps, a framework that we define as the application of agile engineering and DevOps best practices to the field of data management. With DataOps tools and culture shifts, businesses can rapidly turn new insights into fully operationalized production deliverables that unlock business value from data.
We recently shared more about our DataOps model and solutions at this year’s BMC Exchange, our annual IT and business event where we explore the tools and technology that are helping businesses successfully navigate the digital age. This year, we focused on the power of data and how businesses can more effectively use their data to speed their evolution to an Autonomous Digital Enterprise. All session content, including this Day 1 keynote and a great conversation from Day 2 with Daymond John, noted entrepreneur and business mentor, is free to stream on-demand once you’ve registered. We hope you will join the thousands of other participants who have benefited from this year’s solution-focused content and register today.
Outside of your people, your data is arguably your organization’s most valuable asset. Take every opportunity to learn more about the best ways to invest in and capitalize on your data for organizational success. Please enjoy the free and valuable content at BMC Exchange 2021, and let us know if we can help your business operationalize your data!
1 IDC Press Release, IDC Forecasts Companies to Spend Almost $342 Billion on AI Solutions in 2021, August 2021. https://www.idc.com/getdoc.jsp?containerId=prUS48127321
2 IDC Semiannual Big Data and Analytics Spending Guide, H2 20205
3 https://www.statista.com/statistics/871513/worldwide-data-created/
4 https://www.businesswire.com/news/home/20200106005280/en/NewVantage-Partners-Releases-2020-Big-Data-and-AI-Executive-Survey
5 https://www.sigmacomputing.com/blog/top-20-big-data-statistics/
6 https://www.datanami.com/2020/10/01/most-data-science-projects-fail-but-yours-doesnt-have-to/