Real Time Data Adoption Still Slow in the Enterprise
Lower costs, deeper insights, operational efficiencies, and relevant consumer experiences are some of the biggest promises of the digital transformation. But the integration of the IoT, data, analytics, machine learning, and the cloud require real-time stream processing to deliver on these capabilities. As Shelly Kramer explains in How Real Time Stream Processing Changes Everything About Big Data, without stream processing retailers cannot track pieces of data from their customers’ mobile devices and send targeted incentives when would-be shoppers are nearby; financial institutions can’t see stock market fluctuations in real-time and rebalance portfolios based on accurately computed, up-to-the-minute risk assessments; and Ecommerce companies or other financial companies can’t watch machine-driven algorithms and find suspicious patterns that might be suspicious, helping to detect fraud. Real-time stream processing is so essential to capitalizing on digital technologies that Dr. Hossein Eslambolchi, Technical Advisor at Facebook, says “The future of large size data streaming and innovation is more critical than any other innovation for the next decade.”
The demand for real-time data streaming is here to stay. As I described in Capitalizing on Real-Time Data Streaming, the growth of the IoT demands this ability to constantly process and automate mass amounts of data. Even if your company can survive using resting data in the current digital market, you likely won’t be able to survive tomorrow’s.
While all the benefits and requirements of real-time data streaming may be of little surprise to some, the fact that many businesses are still slow to adopt the technology is. Only by understanding the obstacles to change can business leaders hope to overcome them in a timely fashion without losing market share and getting passed by their competition.
A Disconnect Between Capabilities and Adoption
Unfortunately for businesses looking to capitalize on the capabilities of digital transformation, there are typical internal hurdles to overcome before settling on system architectures and server support. Despite the myriad of benefits of real-time data streaming, Matt Asay of TechRepublic reports that in a recent survey 55% of developers said they are choosing new frameworks and languages based on fast data requirements, but still need help figuring out the right tooling to assist them.
Increasingly companies are discovering they are not able to leverage their tools to reach internal objectives as a result of this divide. Now developers and information professionals are working to address a lack of understanding, outdated data management programs, and their ability to access real-time data streams, in order to achieve business goals.
Slowing the Pace of Business
With developers still trying to catch up to the architecture requirements for real-time data streaming it may seem the lag has to do with ignorance. But if management teams and infrastructure professionals have been slow to respond to the requirements of AI, the cloud, and the IoT, the problem is becoming more apparent across the board at this point. A recent survey conducted by IDC revealed:
- 75 percent of respondents believe that untimely data has inhibited business opportunities
- 27 percent indicated it has negatively affected productivity/agility
- 54 percent of respondents claim untimely data limits operational efficiency and slows the pace of business
As Elizabeth O’Dowd describes in Legacy Data Management Slows Real-Time Analytics Adoption more and more stakeholders are recognizing the gap between the capabilities of digital transformation and their internal abilities to leverage these tools. Since real-time environments lower costs, help with data governance, and perform data validation, companies are sacrificing time and quality as they wait to update their legacy systems.
Moving Away From ETL
The legacy data transformation tool ETL (or Extract, Transfer, and Load) has been around since the 1970s and presents much of the underlying design problem with matching traditional technologies with the needs of a real-time architecture. These databases weren’t created for real-time data processing and are designed to run by batch processes. As Andy Patrizio of Network World points out in ETL is Slowing Down Real-time Data Analytics, ETL data has been shown to be five days old, or more, when it reaches an analytics database, making it useless for real-time analytics. In addition, as data requirements for digital transformation technologies grow, ETL is becoming more and more obsolete.
Overcoming the Delays Ahead of the Competition
The competitive advantages inherent in integrating analytics, machine learning, AI, and data management require the backbone of real-time streaming to reach their full potential. While combining batch processing and real-time streaming, and employing edge computing as a method for cutting down streaming requirements, will help alleviate the strain of big data and digital transformation, companies cannot afford additional delays in creating systems to support their technology needs.
As Craig Strong of the Forbes Technology Council points out, developing the capability to learn the fastest, and realize the potential of available data, is the key differentiator of the dominant companies of tomorrow. Or, “If you’re not already connecting, consolidating and analyzing your data in real time for your business to become a learning- and knowledge-based organization, you can be sure that your competitors will be soon if they haven’t already.”
Business leaders who understand the requirements for real-time streaming and work fastest to create the internal capabilities and management processes to support the transition to real-time will have the advantage of legacy processes, relevant databases, and analytics histories as the next wave of digital transformation evolves.
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