Today, big data has become ubiquitous and has virtually reshaped business and operations across a host of industries such as technology, healthcare, ecommerce, law enforcement, medicine and diagnostics, agriculture, weather forecasts, streaming platforms, wearable devices, and so on. It is no longer about leveraging just data storage, cloud services, and communications. Instead, big data has opened vast opportunities for companies to leverage data, enhance customer service, streamline businesses, and rethink the interconnectedness of everything.

The explosive growth of big data can be attributed to the numerous advantages it provides, such as enabling:

• Better strategic decisions
• Enhanced operational process control
• Better understanding of consumers
• Effective cost reduction
• Increase in revenues听

No wonder then the global big data and analytics market is expected to grow at a CAGR of 10% between 2022-2027 to reach听听by 2026. Considering this exponential pace of growth of big data, a CIO or CTO will want to keep ahead of these听ten trends听making waves in the field.

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1. Organizations will focus on adopting data fabric architectures

Digital channels are exploding鈥揵e it for marketing, sales, customer support, or services. Adding to the complexity is the remote style of working. Enterprises now find themselves grappling with a plethora of applications, devices, and different kinds of data infrastructure (think data warehouse, data lakes). In simpler terms, the distributed enterprise lacks a centralized data infrastructure鈥搊ne that seamlessly weaves together all the data available and caters to the organization鈥檚 data and analytics needs end-to-end.

Enter data fabric architectures. This technology is gaining momentum as it can effectively integrate multiple data repositories across cloud and regional boundaries. Going forward, organizations will need to strategize ways to drive a singular enterprise-wide data and analytics management approach that empowers them and boosts delivery time.

2. Decision intelligence will have a 鈥楤ig Moment鈥�

As more and more data-driven enterprises continue to digitize their business processes to gain a competitive edge, decision intelligence will come into focus. According to estimates by the听, around 463 exabytes of data will be created each day globally by 2025. In absolute terms, this is equivalent to 212,765,957 DVDs a day.All the data generated is of no business value if it cannot be translated into actionable decisions and by extension, outcomes. This is where decision intelligence comes into the big picture.

Since machines cannot understand the implications of decision outcomes, there鈥檚 need for human intervention. Enterprises should drive greater collaboration between decision intelligence data scientists and business teams to extract maximum value. When used correctly, decision intelligence can serve as the 鈥榖ridge鈥� between data and improved decision making. In fact, decision intelligence is going mainstream, with听听predicting that听鈥渂y 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.鈥�

Pro tip:听Enterprises need to integrate decision intelligence into their data management strategy and the existing business intelligence stack to measure outcomes鈥搊r else, they risk losing their competitive value.

3. Enterprises will leverage both small and big data to cater to the customers

Not all data decisions will be focused solely on big data. There will be increasing emphasis on combining听产辞迟丑听small data and big data to deliver near-term value and extract strategic benefits in the long-term. For instance, small data analytics can:

• Empower organizations to deliver a hyper-personalized customer experience by using actionable, targeted data to cater to a specific issue or problem that customers might be facing.
• Answer core strategic questions about the business and help understand the best big data applications to use to drive more advanced analytics.
• Help drive data management excellence within an organization as the data becomes more manageable.

Furthermore, it is predicted that by 2025,听. With organizations increasingly using unstructured and structured data together, this trend will become a must-have feature for businesses.

4. Humans and machines will work side-by-side

Technologies such as Artificial Intelligence and automation will augment the workforce across industries and sectors to create a digitally-resilient economy. In fact, research by the听听states that the robot revolution will create 97 million new jobs by 2025.

While AI offers multiple business benefits such as improved learning algorithms, efficient data processing, predictive analytics for trend forecasting, and more, the role of human resources cannot be undermined. This means that while businesses may have to ramp up their investment to leverage responsible and 鈥榮marter鈥� AI, human talent will remain crucial to driving key tasks that machines are unable to perform (yet). These can include informed decision-making, crisis and scenario mapping, spotting anomalies from the data at hand, adapting to adversity, interpersonal skills, and creative thinking.

5. Predictive analytics will Gain Traction

Data is quickly emerging as the world鈥檚 most-valuable resource. That said, it is not enough for organizations to amass gigantic proportions of data. They need to be able to transform the data into actionable insights extracted through powerful analytical tools. This is where predictive analysis comes into play.

Think of predictive analysis at the juncture of big data and business intelligence, allowing organizations to:

• Predict future trends with respect to the market, customers, cloud applications, product performance, among others,
• Leverage AI/ML algorithms to improve data-based decisions and business outcomes,
• Conduct predictive marketing and data mining to target customers in a smarter way,
• Eliminate bottlenecks and issues with respect to operational efficiency, and
• Optimize their internal processes and positively impact the bottom line.

Such is the popularity of predictive analysis that the global听听size is projected to reach USD 35.45 billion by 2027.

6. Data and Analytics (D&A) will form an integral part of business goals

With more and more businesses realizing the powerful value of data, D&A will emerge as a core business function as opposed to a secondary goal, which has long been the case.

This makes business sense as research indicates that companies only end up analyzing听听they have鈥搕he other 88% of data goes unanalyzed. In addition, only听听claim to have forged a data-driven culture, making D&A a top priority among businesses. This becomes even more important when we consider that with big data, organizations can:

• Use data-driven strategies to innovate their offerings
• Predict outcomes more accurately
• Understand how the product is used in the real world and gauge what consumer intent and preference looks like
• Leverage a 鈥榮hared鈥� business asset and drive better collaboration between teams
• Create more opportunities for growth and revenues

7. Real-time data and leveraging Data-as-a-Service (DaaS) will go mainstream

DaaS has been around for quite a while but growing amounts of data volume from sources such as social media, mobile applications, and the Internet is causing a boom in Data-as-a-Service (DaaS). According to research, this market is expected to grow by听听during 2021-2025, at a CAGR of 38.87%.听

DaaS enables organizations to听save on costs, transform unstructured and semi-structured data into structured and meaningful data,听补苍诲听drive agile and secure business performance.听So how does big data fit into the big picture? DaaS when used in combination with big data plays a vital role in empowering enterprises to:

• Gather large volumes of complex data and conduct data analysis,
• Revisit historical data and draw actionable conclusions,
• Process large quantities of data from multiple sources.

These naturally extend a multitude of business advantages such as simplified access to data by the customer, anytime and anywhere, and unparalleled cost-effectiveness when storing data in a secure, centralized location. With the increasing adoption of big data鈥損redicted to grow up to听听by 2027鈥揳cross diverse industries and verticals, DaaS will emerge as a complementary fit to advance business goals.

8. Composable data and analytics will drive data agility

Accelerated digital transformation is encouraging enterprises to deploy AI and big data applications on the cloud. One area that is steadily gaining traction is composable data and analytics. There are numerous benefits to using composable data, including:

• The ability to store and distribute varied resources to remote machines/devices,
• The ability to quickly build flexible, effective, and user-friendly intelligent applications,
• The ability to transform insights into actions,
• The ability to upgrade processes swiftly, and
• The ability to leverage organized IT infrastructures that are scalable, robust, and come with high potential for automation.

All in all, with composable data and analytics, enterprises will be able to build analytics applications for emerging cloud marketplaces, and with the in-demand capabilities of low-code, and possibly no-code, functionalities.

9. XOps will enable enterprises to operationalize business value at scale

XOp can be broken down into two parts: 鈥榅鈥� can signify data, business intelligence (BI), infrastructure, or machine learning (ML) models, whereas 鈥極ps鈥� refers to automation via code.

Whether businesses use DataOps, MLOps, DevOps, ModelOps, or PlatformOps, the XOps landscape is continuously expanding. What is the primary reason for this growth? Typically, it has been seen that most AI-driven and analytics projects go south as the issue of operationalization is not addressed at the right time. This is where XOps is emerging as a key automation strategy and empowering organizations to drive business value at scale, while leveraging the following 360-degree advantages:

• Enjoy productivity and economies of scale using DevOps best-practices.
• Ensure reliability, reusability, integrity and integrative ability of analytics and AI assets,
• Reduce duplication of technology and processes, and
• Enable automation at scale.

The big difference also lies in the fact that these components are now relatively more interconnected (as opposed to operating as silos which was previously the norm) to drive innovation and agility in equal measure. Even in 2022, XOps will continue to drive business value and gather a bigger fan-following.

10. Augmented Analytics (AA) will become important for businesses

As AI becomes well-versed with enterprise information management, augmented analytics will gain importance. A report by听听claims that听鈥淥rganizations are highly interested in capitalizing on innovations in AI, big data, and cloud-based services. Almost three quarters (74%) of organizations hope to invest in the newest technologies in order to improve operational efficiency.鈥�

In other words, AI-enabled BI will pervade all areas of business operations and empower decision-makers to truly focus on what actually matters. The top-four advantages of augmented data management in the context of BI include:

• Greater accuracy: The use of machine learning lowers the chances of statistical mistakes that may occur when manipulating large volumes of multiple datasets.
• Improved speed: AA can boost the speed of processing data by allowing the request processing to begin immediately once the request is submitted at machine speed.
• Reduced bias: As opposed to data scientists who may overlook certain processes and insights due to unintentional bias, AI can work through data more thoroughly and efficiently, without bias getting in the way.
• Increased resources: Augmented analytics can increase the value of the IT staff and the data scientists as they focus more on high-value tasks and create deeper, more meaningful insights.

The Way Forward

As we grapple with the implications of living and working in a post-pandemic world, data dependency will define how businesses leverage and drive growth in the future. Enterprises are realigning their business goals to become digital- and data-first. There鈥檚 greater focus on how to be more mindful about integrating and managing enterprise data to make it easily accessible, trusted, and governed. Big data holds great promise for 2022 and beyond. Enterprises looking to combine human intervention and data analytics and achieve value-driven business outcomes听can explore hi-tech tools such as听听补苍诲听.

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The announcement was made at听, held online October 14鈥�15.

As customer experience becomes an increasingly important differentiator of brands around the globe, many are turning to CDPs to help power unique, personalized experiences for a variety of marketing uses. But too often, this narrow marketing focus has impeded the true potential of an effective CDP. 麻豆原创 Customer Data Platform is planned to go beyond marketing by adding rich context to commerce, sales and service experiences, with relevant timely marketing. In so doing, 麻豆原创 Customer Data Platform aims to deliver personalization, based on its ability to collect and manage customer data. This will enable organizations to know the customer at every touch point and to effectively drive relevant conversations and create lasting customer loyalty.

Connecting, Respecting, Understanding and Personalizing Data for Success

麻豆原创 Customer Data Platform is specifically engineered to tackle four key opportunities to increase brand reach and effectiveness:

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鈥淣o two customers are the same, and no single customer is perfectly predictable,鈥� said Trond Andersen, head of IT strategy and architecture, Elkj酶p Nordic AS, one of the largest consumer electronics retailers in the Nordic countries. 鈥淲ith 麻豆原创 Customer Data Platform, it is possible to create a contextual view of the customer and couple it with a unified profile, to better anticipate their wants and needs as they express them. This will increase the effectiveness of our engagement through real-time data management, while ensuring we are handling the data in a compliant and respectful way.鈥�

Building on a Strong Foundation

麻豆原创 Customer Data Platform is built on the foundation of 麻豆原创 Customer Data Cloud solutions, which are based on Gigya technology. 麻豆原创 Customer Identity and Access Management and 麻豆原创 Enterprise Consent and Preference Management solutions are woven in to help ensure a secure and compliant digital profile. 麻豆原创 Customer Data Platform serves as the connective tissue of the real-time profile, powering a customer insight foundation to deliver relevant conversation whenever the customer wants to engage with the brand.

鈥淲e did not invent CDP, but 麻豆原创 Customer Data Platform opens the concept to a new world of opportunities,鈥� 麻豆原创 Customer Experience President Bob Stutz said. 鈥溌槎乖� Customer Data Platform is one of the most advanced enterprise-grade CDPs. It can truly deliver personalized experiences that nurture anonymous users into known, loyal customers using the customer鈥檚 preferred channels, unifying vast amounts of front-office, back-office and experience data as only 麻豆原创 can.鈥�

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