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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Organizations are embarking on transformational digital initiatives and racing ahead to adopt disruptive digital technologies to interact with their customers and stakeholders. AI-powered tools and platforms become the ideal software for small businesses and big companies to connect to customers. Customer relationship management tools such as chatbots, email bots, AI assistants, intelligent self-service platforms, digital service agents, and other software become a customer鈥檚 first point of contact and offer seamless customer experiences thereafter.

AI Ethics 鈥� the new Shiny Penny in Business

There is no doubt that AI and other new digital technologies can generate lasting value for companies. However, it鈥檚 also creating trust issues on how technology is deployed and used, thus emphasizing the need for ethics in AI. In a highly competitive marketplace, digital ethics has become a key differentiator that is as important to achieving business objectives as delivering outstanding products and services. Adherence to digital ethics is no longer an option.

Ethics is a well-defined system of principles that influences our choices of right and wrong and defines what is morally good or bad. Ethics is a human characteristic, and AI or machines cannot be expected to exhibit empathy and employ ethics, nor can they learn it over time. Thus ethics has to be incorporated into AI-based systems at the development stage.

Dangers that Lurk in the lack of AI Ethics

AI-based systems are intrinsically data-driven, and issues related to the accuracy, privacy, security and bias associated with the use of data have popped up from time to time. However, formulating standards to use AI ethically is not an easy task. In a world that is becoming increasingly globalized and operating online, countries and organizations are grappling with the intricacies of developing ethical guardrails for AI.

Industries across the spectrum are impacted by the lack of codifying standards in AI ethics. Some听sectors听where the implementation of AI ethics has become essential are the following.

For insurance and financial business analytics services

The financial services industry is one among the many that face the ethical concerns raised by AI. Insurers, banks, fintech and other financial institutions are automating services with AI. In areas such as wealth advisory, risk management, fraud detection and credit rating, AI is supporting or even stepping into the shoes of human decision-makers. Behavioral sciences techniques woven into AI algorithms have proved to be successful in changing customer attitudes towards money. They have proved to be helpful tools to nudge individuals to focus on savings or to track their spending patterns and work on financial planning. However, ethical concerns arise when the nudges powered by AI become unethical tools to manipulate behavioral change.

AI speeds up claim and application processing, saves money and supports timely fraud detection. However,听the risk of bias听is very high in the AI algorithms making assessments. There is a听lack of interpretability and transparency听on how an AI algorithm comes to a decision in AI software for small businesses and large organizations, making it difficult to identify biased or discriminatory behavior. The bias could also be unintended. It could be representative of the prejudice present in the social system. The machine does not understand or consider removing the biases but just tries to optimize the model. Often the data fed into the AI program will not be a perfect representative sample when there is a limited dataset from certain minority segments, which leads to algorithms tending to make sweeping generalizations.

AI-based business analytics services can make assumptions about the risk profile, habits and lives of people. Applicants can be charged excessive premiums after being categorized as high-risk or denied loans due to low credit scores. These biases result in discrimination against ethnic, gender and racial minorities.

For healthcare business analytics services

The use of AI in healthcare is relatively new and has revolutionized healthcare. From diagnostics and imaging to apps to assess symptoms to workflow management in hospitals, AI is used in a wide range of clinical and operational applications in healthcare. Explosive growth is predicted in the use of AI in healthcare in the years to come. However, with this growth comes the dangers and challenges in using AI ethically.

AI chatbots and health apps provide a range of services and collect and analyze data through wearable sensors.听Ethical questions about听user agreements听arise in such a situation. Unlike the traditional informed consent process, the user agreement is signed without a face-to-face dialogue. Most people routinely ignore user agreements and do not take the time to understand them. Frequent updates to software also make it difficult for individuals to follow the terms of agreement once signed. The information from AI chatbots or health apps is sometimes fed back into clinical decision-making without the user鈥檚 knowledge.

Another big challenge for AI in healthcare is听transparency. Transparency is essential to ensure patient confidence and trust between clinicians and patients. In AI programs that use genotype and phenotype-related information,听biases听could result in false diagnoses and ineffective treatments that could jeopardize the safety of patients. An AI algorithm trained majorly on data on Caucasian patients and limited data on African-American patients can give inaccurate diagnoses or treatment recommendations for the African-American populations.听Data sharing听could sometimes happen outside the patient-doctor relationship, such as for clinical safety testing of health apps or with friends and family members. However, patients are often not clearly informed about the processing or sharing of their data.

For military business analytics services

Countries worldwide invest millions of dollars in the research and development of modern technology for military applications. The increasingly intelligent and autonomous AI has become a favored choice for many. AI-equipped autonomous weapons have changed the theatre of war. Ethical questions about what a weapon is allowed to do on its own and who is听accountable or takes responsibility听for what it does on its own are being discussed by strategists across the world. Development and deployment of AI-enabled weapons require significant oversight, responsibility and judgment as compared to other systems because of the ethical issues involved.

An accidental or small skirmish can escalate and become a full-fledged conflict due to autonomous weapons with pre-programmed goals making decisions on their own. Malicious听manipulation听of AI systems in autonomous weapons can trigger a cascade of unintended actions and cause large-scale harm on the battlefield. The inhumanness of war is amplified when states that believe in using AI weapons unleash them against an adversary who does not use them and is ill-prepared for the impact. The听limited understanding听of what AI weapons are capable of and the lack of ability to recall a system once triggered only compound the harm. AI systems cannot treat opponents with dignity, analyze context to distinguish between non-combatants and combatants, or recognize signs of surrender. Ethical regulations can restrict the use of fully autonomous AI weapons and reduce the incidence of immoral violence.

Tech companies partnering with the military of a country are governed by a code of ethics pre-dominated by the moral biases of the employees. Terrorists, geopolitical enemies, rogue nations, etc. can whip up patriotic fervor and impair the moral compass of a developer. There is significant concern about the dangers of AI-fueled arms races by rogue nations and the amassing of state-of-the-art AI weapons by dictators and terrorists.

Conclusion

The concerns discussed above are relevant to all industries. AI implementation in software for small businesses and large organizations should take into consideration legal and ethical implications. AI tools are developed by public-private partnerships and large private technology companies. Although these companies have the resources to build the tools, they are not incentivized to adopt ethical frameworks when designing them. Even if regulations are crafted to fight bias in AI, they will have to be implemented with other that act as watchdogs. However, the role that technology will play in enforcing these regulations has to be studied and monitored.

AI is ethically neutral; it is the human beings who develop AI systems that input their individual biases and opinions into AI machines. Developers should be transparent about the data used and its shortcomings. The central tenet around which the development of an AI program or system revolves should be algorithmic accountability.听Sufficient controls should be put in place to ensure that the algorithm performs as听expected.

Ethical and legal concerns should be tackled early in the development lifecycle. Incorporating ethical analysis into the development program could reshape processes and create new costs;听however, developers and decision-makers must recognize that this approach will reap tangible and rich benefits in the future.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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Data and Analytics

All the massive data gathered by organizations requires analysis. Traditional methods of computing did not allow for quick analysis of such large amounts of data. However, with the advent of new technologies such as cloud computing, organizations can now process data quickly. Data analytics has also become more meaningful with the help of these technologies, and vast amounts of data can now be made more sense of.听

With products and services that come enabled with internet connectivity, various kinds of data can be collected and instantly analyzed. This data from the customers includes:

• purchasing and search behavior听
• personal preferences and details听
• methods of communication听
• social media usage patterns听
• location information

Once all this data has been analyzed by machine-learning algorithms, the services offered by an organization can be adjusted to suit individual needs. The services offered to the customers can thus be tailor-made.听

It is thus the analytical results of data that can be used by an organization for different purposes. Data analytics are, therefore, the digital processes that use data and insights from different channels. This data is ultimately used to generate value for the customers in the data-rich business environment of today.听

Therefore, like oil, data can be refined through analytics and turned into a highly valuable asset. And this has never been as true as it is today. Data has become one of the most important commodities for businesses worldwide. Organizations can use the data to gain a competitive edge in their business by understanding the environment in which they function.听

Organizations around the world are increasingly recognizing the importance of data. For example, 81 percent of respondents in China believed that data analytics is either extremely important or very important to sustain a competitive business, according to a听听conducted last year. In North America, 34 percent had a similar opinion.听

Innovation driven by data has been predicted to be one of the most significant ways that would help to improve social welfare and hasten economic growth in the 21st century. Data gathered through greater interactions between business and customer is helping to bring together the two. This is leading to more accessible and customer-friendly business environments.听

In another听, half of the executives working in the global travel and hospitality industry believed that customer data analytics was crucial to the success of their companies and helped in achieving a competitive advantage in the business. While most of the executives believed that data analytics was important to varying degrees, only one percent of the respondents believed that customer data analytics was not important at all. These trends show how data operations and analytics are becoming an essential determinant in the competition in businesses.听

The high value that is being placed on data also reflects in the increasing investments in data analytics. Two years ago, the global market for big data and business analytics was valued at $168.8 billion and by end-2021 it鈥檚 expected to grow to $ 215.7 billion. More than half of the spending in big data analytics is likely to go towards services. IT services are projected to make up around $85 billion. Business services will account for the rest.

So how can data help your business to gain a competitive advantage?听We give you 5 ways in which this can be done:

1. Data helps you find the right customer

Many businesses waste resources by targeting the wrong group of people. Basically, sales teams try to woo customers who do not require their services. This leads to wastage of resources and time. Finding the right customer for your business is therefore important. Data and analytics can help your business by attracting the right kind of customer.

The right customers 鈥� who need your solution or service 鈥揾elp your business stay profitable and help by providing large-scale data resources. This data forms the raw material for deriving greater insights through data analytics. This data can include the user’s behavior, preferences and shopping habits. This user-generated content, created by the customers themselves, then becomes a repository for the business to glean useful information from. It can ultimately help the business in multiple ways, by allowing it to听

• design the right kind of marketing campaigns听
• target the right group of people
• deliver their service through the right kind of channels

Personalized data resource allocation mechanisms can also allow a business to understand the needs of the customer better. It also reduces the effort required from a customer to have a service delivered to them through predictive and proactive executive frameworks. Such frameworks can be put in place only through data and analytics.听

2. Swifter and better decision-making

Data can be a massive tool for any business if it is used right. One of the most important advantages that it equips an organization with is better and quicker decision making. This is achieved in multiple ways. One of them is by helping to deliver the right data to the right people at the right time. For example, relevant data from your feedback and customer service department can be quickly diverted to the design and production section to convey the faults in a product. After analyzing customer preferences, a product can then be designed which is more suited to the customer needs. All this requires the right information to reach the right people within your organization. Data analytics help to organize all this information for dispatching to the relevant personnel.听

Another way that data helps in decision-making is by helping your business to understand what the customer is looking for. Businesses gather a lot of data from the customers and the markets. This data, when analyzed properly, can help businesses to get a sense of the customers鈥� minds. Data-driven analytics do this by helping your business to understand just exactly what your customers need, when they need it, and how to deliver it best. This data can also help the business to understand the kind of product and service they need to supply, and how to market the product. This data can also be used for tracking current developments and for forecasting the future. All these help the business to make better decisions in a shorter time.

Data-driven analytics can also help struggling businesses to understand the reasons for slow or stunted growth. Once the problem is identified, data can also help the business find the best remedial measures to recover growth.听

3. Helps you get closer to the customer

Businesses are increasingly moving towards platform economies. These are business and economic projects and activities that are carried out digitally and facilitated by platforms. Online platforms are one of the most essential components of such a platform economy. These online platforms are constructed with the help of a large scale of personal data resources. Still, they can also run and function optimally only through a consistent supply of data. This is especially true for businesses dependent on greater and constant personal data connectedness with their customers. Examples include car-hailing platforms, payment platforms and food delivery services. This is also true for businesses that establish greater connectivity with their customers through social media platforms. In order to serve their customers better, these businesses need to stay in constant touch with them. This requires a ceaseless gathering of data. It also calls for sifting through enormous amounts of data, all of which can only be managed through better data analytics set-ups.

Once a business begins to work this data in a comprehensive manner, the business can deliver better services to the customer.听

Data and analytics can also help an organization to identify a niche and narrow market segment. Data and analytics can enable an organization to predict and foresee changes in the markets, thus allowing the firm to be a leader in markets where no one else has ventured before. Niche market needs can be met this way.听

4. Helps in creating synergy within your business

Better coordination between the different departments of your organization can also help to fulfill your company鈥檚 potential. This is possible when the company is functioning at an optimum standard. Data analytics also helps in achieving this by compartmentalizing work. As data is gathered from the market and customers, analytics help the company devise executive frameworks. This means allotting the right work to the right department. Once this balance is achieved, your business can generate a larger supply with higher efficiency. As hassles between multiple stakeholders are removed, data and analytics can help to deliver quicker and better.听

A well-coordinated data analytics module can also help to generate more demand from the consumer. This is achieved through a better understanding of the target group of your service. Greater interactivity is also reached, as the consumer becomes the content or service supplier by making the data available. This two-way process achieved through data helps to attain a synergy that is a characteristic of a successful and dynamic business operation.听

By keeping track of customer feedback on a product or service, data and analytics can also help an organization follow up on its services. By gathering data from customers using a product, their habits of usage, and their responses and reactions, data analytics can help a business understand the customers鈥� needs better. By providing solutions to problems that arise with a product, the organization can also keep in touch with the customer and instill a sense of accessibility in them. This also helps to create a friendly image of your brand and can help in generating customer loyalty and good word of mouth.听听

5. Helps you develop long term strategies and create value听

The social and technological systems of the modern world are moving along a path of convergence. This means that technological practices and habits are becoming a part of our social customs and practices. From interactions between families and friends, to managing and carrying out day-to-day business activities and planning, technology has come to be involved in everything. This is why creating a long-term strategy for data and analytics can allow your business to stay ahead of the competition. This can be developed through data.听

Data can help the keen-eyed to predict the future. By having an analytical framework ready for the data you gather, your business can have a tool for keeping customers happy and for acquiring new ones. It can also help you to avoid pitfalls and predict errors. Resources can thus be saved, and faults avoided.听

Developing long-term strategies and avoiding errors can help businesses in value creation. Value creation and market analytics driven by data are therefore interlinked. Value creation is the expectations that customers come to associate with a brand. It is cultivated by a business over a while and amounts to a promise of value to be delivered to the customer. It is one of the basic tenets of brand loyalty, and if a business expects its customers to keep returning to them, value creation should be nurtured and sustained. The applications derived from data-driven planning and investments by global companies also lead to the creation of value. In addition, they also help to satisfy customers鈥� desired needs better.

Data is associated with value creation in a dynamic process. On the one hand, promoting value creation has picked up pace in recent years due to increased data-driven analytics. On the other hand, data has also acquired significance due to the greater value that customers associate with a brand. Customers have also come to expect a minimum standard of technology-friendly approach from services. As such, they expect spontaneous value from the businesses they interact with. This two-way communication process also produces a large volume of data through听. These include-

• The use of RFID tags to analyze customer store movement听
• Real-life, real-time and large-scale assortment optimization听
• Analyzing data from customer purchase records over a large volume of product categories
• Gathering and analyzing data from the shopping behavior of the customers with an in-store mobile phone and connected apps.

All this data can be used to generate value for the customers.

According to the Marketing Science Institute, understanding a customers’ 360-degree view of preferences has become one of the most highly valued priorities for businesses worldwide. This is because it helps to generate value for customers. While data has made generating value easier, it also allows the business to maintain this value in a data-driven competitive business environment. This is particularly helpful for businesses that involve direct and intimate contact with customers. Businesses like retail can get the most out of value creation by听.听

Business organizations and brands use their valuable and identifiable resources to attain a competitive advantage by creating value for customers. These resources may include a promise of better service or a brand appeal that customers associate with a firm. However, versatile resources like the knowledge of a customer鈥檚 needs can help an organization grow by helping it understand how to position its services to best suit the market. Data-driven analytics does this by re-combining the resources of an organization in innovative ways to strengthen its capabilities. While the resources of an organization can be used to set up the infrastructure for data-driven analytics, the data can be used to service the customers better. Data can also serve to guide an organization on how to structure the infrastructure.听

Combining traditional and knowledge-based resources also enables the organization to offer meaningful experiences and returns on investments to the customers. These returns are based on current trends. Data-driven analytics also allows the delivery of personalized and individual need-based experiences. This is especially true in modern businesses. The contemporary business consists of an environment where competition in business is high and appealing to customers easy but not sustainable over a long period.听

By acting as a paver for the strategic and executive roadmaps of your business, data and analytics help your organization achieve innovativeness and performance in the data-rich environment of the modern world.

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This news is undoubtedly welcomed by small and medium-size businesses. But only those that can anticipate every nuanced shift along the way will gain the competitive advantages necessary to stay ahead, including improved customer and employee experiences, product and service creation, tight customer connections and fewer skill gaps.

Although every business leader knows that such predictive insight comes from data,听听conducted during the first few months of the pandemic revealed the importance of understanding it accurately and quickly. The study reported only 32% of medium-size businesses are acting on data-derived insights, which could be attributed to struggles in either interpreting data with analytics tools or supporting analytics-based decision-making altogether.

Building data insight with interconnectivity

A considerable challenge to becoming a business driven by data insight is gaining the confidence of the employees who use the information. Data should be viewed as a prerequisite for all decision-making, never as a nuisance or an afterthought.

Ultimately, fostering such a data-driven mindset requires a strong IT infrastructure that helps ensure data is complete and accurate and shared and provided freely and securely across functions, external partners, suppliers, and customers. This step toward interconnected alignment of knowledge, visibility, and insight allows the workforce to immediately understand and embrace the optimized collaboration, transparency, predictability and continuity that today鈥檚 technologies offer.

The IT infrastructure should include three foundational elements:

1. Consumer-grade analytics

This evolutionary step toward 鈥渁nalytics for everyone鈥� allows decision-makers to access and analyze data, predict, and plan scenarios, and report insights, outcomes, and lessons learning all in one application. Intelligent capabilities 鈥� such as natural language querying and processing, machine learning, and predictive analytics 鈥� can also augment and accelerate decision-making without requiring additional training in data science.

2. One platform for data management and analytics

Bringing data management and analytics together on a single business technology platform reduces the complexity of maintaining multiple technologies, such as limited communication, data sharing, and collaborative action taking across departments. This addition to the IT infrastructure provides the structural support needed to collect, integrate, and analyze information in a landscape that includes legacy systems, multi-cloud applications, public and personal data sources, sensors, and smart devices.

3. Embedded enterprise analytics

Don鈥檛 let this phrase fool you: the word 鈥渆nterprise鈥� does not mean that embedded analytics is just for your largest competitor.听It鈥檚 about providing medium-size companies scalable, cross-departmental access to a 360-degree view of the business without switching from one application to another to get work done and collaborate with experts and stakeholders. This capability combines business intelligence, augmented and predictive analytics, and planning capabilities into one cloud environment and in the context of business processes.

Reaping the rewards of interconnected intelligence

By augmenting their IT foundation with these three elements of data management and analytics, employees can make decisions that not only optimize their specific area, but also help each other succeed. Take, for example, the relationship between workforce management and spend management.

HR analytics typically focus on recruitment, talent and performance, learning and development, and compensation and retention. But with the assistance of intelligent capabilities, HR leaders can correlate that traditional information to health and safety compliance, travel and expense management, procurement, and project assignments.

Including people data in these critical business indicators allows professionals outside the HR function to identify and solve potential issues early and generate value as quickly and cost-efficiently as possible. Plus, departments can measure and predict the full impact of their spending decisions while eliminating organizational blind spots, minimizing maverick buying, improving supplier performance, and optimizing the cost of quality, goods sold, and sales.

Bringing to life a stronger, more resilient business

Just imagine the possibilities when every business function can access and act on connected, integrated data from a single landscape. Will your operations realize a responsive supply chain, deliver an engaging and always relevant customer experience, help ensure every employee is successful, or innovate new product or service?

Whatever the answer, this level of interconnectedness unquestionably provides a unique differentiator that medium-size businesses need 鈥� and can acquire with ease 鈥� to positively shift the trajectory of their recovery and growth.

Discover how your business can achieve these goals by consulting the Oxford Economics report, 鈥�.鈥澨齈lus, you can learn more by accessing our guidebook, 鈥�.鈥�

Mario Farag is senior director of Marketing for Analytics at 麻豆原创.
This article was originally featured on Forbes, .

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