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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Companies need to inculcate a thriving data culture across functions — including business operations, product, marketing and human resources — so they are equipped to make informed decisions. This means embracing a fundamental shift in mentality.

As a company鈥檚 multiple departments become stronger at addressing and extracting at least basic data queries and visualizations, its data analysts and scientists will get the space they need to focus on their core responsibilities鈥攊mproving data models to provide meaningful analyzes beyond simple data pulls. In other words, developing data intelligence support systems to ensure timely availability of data services.

These data services come in different forms. The high-level metadata categories include:

• Behavioral:听Keeps track of who uses the data and how
• Technical:听Displays the definitions of a schema or table
• Business:听Policies on how to properly manage the various types of data

When a new version of a dataset is developed, the provenance shows the relationship between two versions of data items (also known as lineage.) Behavioral metadata is extremely significant since it represents an organization’s human expertise around data. It demonstrates how individuals can use data to derive insights and learn. The animating spirit of an organization’s unique data intelligence is formed based on how employees use data.

Organizations would do well to adopt the听following approaches听to building a strong data culture:听

A committed top management

Data-driven culture in any organization should begin at the top. The senior-most executives and managers need to establish that decision-making, for business-critical moves at least, need to be based on data, and that this approach should be the standard, not the exception.

Choose metrics with caution and ingenuity

Leaders can significantly impact behavior by selecting what metrics to monitor and what they expect their staff to use. If a company can benefit from predicting pricing changes by competitors, it should have a team focusing on regularly drawing up specific forecasts regarding the amount and direction of such shifts. It should also keep track of the accuracy of the forecasts, which will improve with time.

Data scientists in management

Data scientists should be hired from within a corporation to leverage their knowledge gap understanding. If the analytics team operates separately from the rest of the company, it will find it difficult to create value. Companies can address this challenge by ensuring that its data scientists either:

1. Have line management experience, or
2. Are given line management training and experience.

Accuracy of data

Decision-makers need to confront possible sources of uncertainty head-on: Is the information accurate? Is it possible that there aren’t enough instances for a valid model? How can elements like growing competitive dynamics be included when there are no relevant data? To understand the importance of this, consider a supermarket chain. If the inventory data is inaccurate, the procurement department will be misinformed, leading to supply problems and a fall in revenue.听

To prevent this chain, data scientists should be able to get accurate inventory data in time so the procurement team can take appropriate actions based on that data. This requires data scientists to check for accuracy on a recurring basis to ensure that the data generated is reliable and actionable.

Specialized and focused training听

Specialized training should be provided only when needed. Many businesses spend on bang-for-buck training, only to have their employees forget what they’ve learned if they don’t put that to use straight away. While skills such as coding should be included in basic training, it is more beneficial to teach employees specific analytical tools and ideas such as proof of concept only when required.

Consider a shelf stocker in a retail store who now needs to fill in as a cashier. The training for the cashier position should be given just in time and should be based on data. This way, the training will be fresh, and the job performance efficient.

Quickly resolve data-access concerns

Data access should make data easy to use. Only data that鈥檚 useful or necessary to a particular employee should be made available to them. If universal data is shared with all employees, the information might prove complicated for many, and they might not use it at all. On the other hand, if relevant data is shared with employees in an understandable format, it is likely to be used more frequently and meaningfully.

A task becomes a choice if it benefits an employee directly鈥攕uch as saving time, avoiding rework, or retrieving often required information. For example, sales personnel will not have use for accounts payable data. But give them customer data in an easy-to-understand format, and they will likely put the data to great use, making for a data-oriented culture.

Trade flexibility for stability

Be prepared to give up flexibility in exchange for stability. Many companies that rely on data for their decision-making have a variety of data categories. But if each employee has their preferred information sources, metrics and programming languages, that would be disastrous for the company. Trying to harmonize somewhat different versions of a measure that should be universal can consume much valuable time.

Also, inconsistencies in how different modelers work have several implications. If a company’s coding standards and languages differ, for example, every move by the analytics personnel would necessitate retraining. Internally sharing ideas might also be excessively time-consuming if that continually requires translation. Instead, businesses should use canonical measurements and computer languages.

Alternatives appear too dangerous. Companies鈥攁nd the divisions and individuals who make them up鈥攆requently fall back on habit. Data may be used as evidence to support assumptions, providing managers with the confidence to venture into new areas and procedures without incurring risk. Merely wishing to be data-driven is insufficient. Companies must create cultures that allow this attitude to flourish. Leaders can foster this transition by setting an example, modeling new behaviors, and setting expectations for what it means to make data-driven choices.

Get employees excited about data

If employees are given general training on data and data use, they could get bored and leave the task altogether. But if companies can link the training to the immediate goals that each department needs to achieve by using data-driven techniques, they could get their employees excited about achieving those targets.

For example, if the sales department is taught to use data to shortlist clients, they can use the technique to achieve short-term goals, and will be excited to learn in the future as well.

Because each organization’s culture is different, you’ll need to create a custom stack of solutions for possible inconsistencies due to the requirement of the finance department’s buy-in on new investments. That is why it is critical to tackle it correctly.

As a start, make a list of the solutions you’re utilizing and see whether what’s working for one business unit can be extended to another. This entails adapting solutions to new use cases within the company for many software firms. That won’t always work, and you’ll probably need to invest in new tools as well, and so you should audit your organization’s goals and create a framework based on what you learn.

While this is a lot of additional responsibility, it allows departments leads and teams to work closely with one another, encouraging innovation and deeper business insights. After squeezing years of digital change into mere months because of the pandemic and subsequent lockdowns, IT executives have a chance to emerge as equal partners in creating cross-departmental vibrancy and development.听

Conclusion

While corporations have always been interested in their numbers, in a data-driven culture, the degree of data utilization is exercised at a higher level. The major goal is to enable all employees to actively use data to improve their everyday work and to fully maximize a company’s potential by creating decisions that are more successful, projects that are more effective, and competitive advantages that are more obvious. One way to do this is by enlisting the services of an experienced partner like 麻豆原创. With the help of听听or by using听, support your organization in its journey to mine wealth from numbers.

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