Data is the lifeblood of enterprises today. From insights on customer experience, processes that need optimization, supply-chain innovation, consumer behavior, workflow management and more, businesses must invest their efforts on data science to unearth critical business information. With enterprises looking to extract business value in real-time from data in motion, data science is gaining popularity.
Data science adoption for business
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. It is a “concept to unify statistics, data analysis, machine learning, and their related methods” to “understand and analyze actual phenomena” with data.Data science can help enterprises in the following ways:
Real-time data-driven decision making
Growing opportunities to collect and leverage digital information have led many business leaders to change how they make decisions – relying less on intuition and history and more on in-the-moment data. By looking at real-time data and insights, tactical, strategic, and operational decisions can be carefully reviewed to much more positively impact the bottom line. Leadership can also use streaming data to factor in predictive analytics and look at real-time KPIs to better understand employee and business performance.
Better operational efficiency
By tracking systems, products, and equipment performance in real-time, quick decisions can be made that can significantly affect efficiency. By understanding which operational parameters impact overall business performance, decision makers can be sure to track, measure, and tweak them accordingly, which can decrease costs or lead to smoother and faster processes.
Enhanced customer satisfaction
Customer intelligence applications can be a business’ greatest tool. With the capability to track individuals and their actions, companies can harness this technology to create relevance and targeted customer experiences. This is entirely possible if data is analyzed in real-time.
The ability to ingest, analyze, and act on high-volume, multi-structured data in real-time is quickly becoming a ‘must have’ capability for enterprises across industry verticals to surpass or in many cases maintain parity with their competitors who are deploying these technological advancements as part of their new enterprise data architecture blueprint.
Challenges for data science adoption in real-time
While there are multiple tools and technologies to help businesses make better data-driven decisions, enterprises are still challenged to adopt data science and machine learning. Some reasons that affect data science adoption are:
- Choosing the right data science tool
- The learning curve of technologies around data science
- Scaling data science models to handle high volumes
- A shortage of data scientists
While the challenges can become overwhelming, this also presents an opportunity to create a breakthrough. Enterprises need a futureproof holistic solution framework that lets them:
- Optimize business operations and efficiency and minimize time to delivery
- Connect and act to achieve customer satisfaction
- Prevent threats and risks like frauds and security
- Predict failures and prevent losses
- Provide choices to solution developers
- An end-to-end development framework for all kinds of users
Using data science in real-time for improved business decisions
As enterprises embed analytics into their operating models, businesses get more insights-driven, deriving actionable knowledge from analytical models and algorithms. The Internet of Things promises operational efficiencies, lower costs, and relevant customer insights to businesses with real-time analytics. However, integrating advanced analytics, artificial intelligence, and the cloud require real-time stream processing to deliver on these capabilities.
Low-cost streaming analytics plays a vital role in interpreting IoT data. To derive insights from diverse data sets across multiple sources, enterprises need to have a real-time streaming analytics solution that can process data as-and-when it is generated, allowing businesses to sense and respond in real-time.Enterprises have multiple options to analyze new data streams in real-time environments, like:
- Scale-up enterprise IT to cope with new data streams
- Adopt analytics-as-a-service to compensate for gaps in data science skills
- Invest in more cost-effective storage and analytics solutions
- Use cloud computing for more flexible data management
Enterprises will have to identify what data should be analyzed, the time frame in which it must be analyzed, and what infrastructure to use to carry out the analytics. One of the key challenges for decision makers is to understand what constitutes good data science, and how the insights can be used for critical evaluation and business decision making.