Data analytics has proven to be a critical part of modern businesses. It can help improve decision-making outcomes for almost any type of decision, which can be micro, macro, strategic, operational, cyclical or tactical decisions that will help propel a company to the best possible heights. Data analytics can help unearth new solutions to problems and opportunities that business leaders would not have found if they continued using conventional methods. Progressive organizations employ data for various things and therefore must rely on data from within and outside the organization to make smart and informed decisions.

How companies use data analytics

Data analytics is a catalyst for digital strategy and transformation. It enables faster, accurate and relevant decisions in the complex and delicate business environment. As such, businesses use it for various activities. When employed properly, it can help address many business issues and offer invaluable insights into business and customer behaviour. Further, data analytics can help your business understand everything starting from how products can be managed and running of PR campaigns to how to mitigate risk, enhance security and increase brand awareness. Below are some ways that big data analytics can be used.

1. Informed decision-making

One of the leading benefits of data analytics is using data to justify business decisions as stated above. The process involves two phases, the first being predictive analytics to help suggest what can happen in future based on the gathered data. The second one is prescriptive analytics which can be used to indicate how a business should react to the forecasted changes. For example, businesses can determine offerings and pricing based on customer data to raise sales.

2. Improved efficiency

Most businesses employ big data analytics in streamlining operations. They use it to collect and analyze data relating to business activities like supply chain by identifying where delays and bottlenecks originate from and predicting where the problems might originate from in the future and possibly propose approaches to dealing with them. This enhances operational efficiency and the elimination of bottlenecks. Furthermore, through timely identification of problems, it is easy to address the challenges in time.

3. Mitigating risk

Like the way analytics can be used to identify and predict business inefficiencies, it can also be used to highlight possible risks and devise preventative measures. Retailers for example can use big data and statistical models to determine the stores that are at the risk of being targeted by shoplifters and enhance security in the stores that are found to be at a higher risk. Data can also help mitigate financial risks in addition to physical risks. It can help limit losses by aiding do things such as determining sell clearance of items and reducing inventory.

4. Improve security

In the age of social media and the internet, businesses face countless data security threats. However, with the advancement of big data analytics, businesses can enhance their security posture by predicting possible sources of compromise and preventing them from getting into the business and causing reputational damage.

Businesses can use data analytics to analyze and visualize a company’s audit logs to find out the origins of past cyber-attacks. The data can then be used to identify the vulnerabilities within the cybersecurity processes and suggest possible fixes.

5. Predictive analytics

Predictive analytics is used to deal with probabilities and predict a series of outcomes over time. It can also be used to highlight uncertainties related to various outcomes. Predictive analytics tells us what we should expect and addresses questions about what is likely to happen. This analytics relies on techniques like predictive modelling, regression analysis, pattern matching and machine learning.

Big data and analytics have become a success story for some organizations. However, most of them have never realized the full potential of big data because the real power of this technology has been restricted to small parts of their operations and businesses. Therefore, while others have immensely benefitted from big data, some are still struggling to see how it will help them. According to Gartner, only 20% of analytics insight will deliver the business outcomes this year. This low percentage of delivery is due to various mistakes. Due to this challenge, this article lists some mistakes you should avoid in your big data initiatives.

1. Failing to identify the right business use cases

Failing to identify the correct business use cases and establishing clear criteria for success and KPIs is one of the failures that most organizations make; thus, their big data initiatives and implementation fail. For an organization to effectively capture feasible use cases, they need to put in place various measures, including working with different businesses and leaders and establishing clear success criteria. Furthermore, after brainstorming, companies need to develop a list of over 30 companies to use as use cases. They should then select the most relevant use case based on viability, the complexity of analytics and potential return on investment, among other aspects.

2. Focusing on short-term gains

While short-term gains are good, they are not enough for current times. The information age we live in means that every decision, regardless of the industry in which an organization operates, must be backed by relevant and authentic data. This means that the era of personal intuition and personal experience being relied upon to make decisions is in the past. Organizations now realize that the data collected from customers has immense value. Information is an asset that must be properly managed using the available technologies, which simplifies the collection of data, auto-scaling and using technologies like AI. However, most companies tend to focus more on short-term value from the available tools and ignore everything they would achieve in the long run.

3. Failing to focus on visualization

Visualization is a game-changer in management, and data scientists should dedicate most of their time to understanding the data through various visualization approaches. Sadly, this is not always the case because data scientists sometimes fail to dedicate much time to understanding data through visualization. In the era of heated competition and reliance on data for decision-making, visualizations are crucial in identifying patterns and making the right decisions. As such, companies must hire data scientists who know the goals of visualization and their basic principles.

4. Lacking a central authority

Regardless of the size of an organization, be it big or small, accuracy and quality of data is a key issue that is always recurring. Data is always ridden with inconsistencies and duplications, which requires an effort to clean. One way of maintaining the standards in data collection is to have a central authority to carry out oversight. This will ensure duplications, incorrect data usage, and bad input are avoided altogether. As a leader, establish a committee or give the duty of keeping data clean and training staff that will use the data. Since data is a crucial tool for any organization, leaders should give it utmost attention and never shy away from investing in it to bring the desired change.

Generally, although there is no doubt about the benefits of big data, many organizations have not achieved it. Companies must therefore assess how big data initiatives may fail and approach the implementation with robust strategies that will help mitigate the known risks.

Data analytics projects can be challenging to many organizations, especially those entering the murky waters of data analysis for the first time. While you may think data projects need to be complex or showy, that is not always the case. Rather, the most important thing you should do is demonstrate your skills using data sets that interest you. The good news is that any organization, regardless of the time they have spent, is that data is everywhere, and you need to know where and how to find it and what to do with it.

As you seek to use data analytics to the advantage of your business, here are a few data analysis projects that are gaining attention. But first, there are two things that you need to set up before you can succeed in analysis. One of them is cloud computing. Moving data to the cloud has substantially levelled the playing field among the organizations. Furthermore, it gives users the power to use cutting-edge services offered by companies like Google, Amazon and Microsoft. The second is to establish parallel computing distributed cluster servers to increase memory and storage capacity.

Below are some of the projects that are gaining attention.

1. Fraud detection projects

Even with the advancement that we presume we have made in detecting and solving fraud, hackers still find a way to get into the systems and steal sensitive information or even money. This is especially the case in text messages, emails, financial systems and communication platforms. Fraud detection is a useful tool for identifying the issues and addressing them quickly. However, this cannot be okay without the right data analysis. Big data analytics projects geared towards bolstering fraud detection are gaining momentum.  

2. Crime prediction

Like fraud detection, big data projects aimed at predicting fraud are gaining ground. This technology uses machine learning to predict and detect crimes. Machine Learning algorithms analyze crime data, subjects and locations, and descriptions of the perpetrators. Big data is used to train models to improve machine-learning capabilities to predict crime. In metropolitan areas, systems are used to monitor traffic to ensure everything flows as efficiently as possible and to report any bottleneck.

3. Predicting and simulating traffic

Big data projects focused on simulation and prediction of traffic in real-time have proven beneficial in many ways. Traffic simulation has been proven to be successful in models. However, predicting route traffic is a long-standing issue. It is a complex project that requires vast volumes of data, involves high latency and has cost implications.

4. Nuclear physics and data analysis

While the world we live in today may appear simple, it is a complex place from the big data point of view. Companies like CERN regularly release vast amounts of data for analysis and research to the public. This data is diverse and captures many dimensions, each with various data points. The field of nuclear physics is one of the beneficiaries of big data technologies. These technologies are used to analyze large data volumes. With big data analysis projects, the field of nuclear science has seen much improvement in university research and made institutions competitive.

5. Employee salary data analysis

One of the biggest ways to understand how an organization or a city works is to analyze the salaries of staff residing in it. This will provide evaluation criteria for the management to understand how individuals are paid. A close look at the employees and their salaries shows the on-demand jobs, and the job offers with the highest compensation packages. This helps job applicants get an idea regarding which jobs will give them better pay.

Technology is advancing at a fast rate. The same can be said about the emergence of new devices and their data. With vast data emerging from different sources, new approaches are adopted to process data and gather insights. As such, data analysis has gained fame. While big data analytics is a good thing for your business, mistakes can be made in the data processing. As you seek to implement this technology in your organization, watch out for these common analytics mistakes in your operations.

1. Use of data alone when confirming ideas

One of the biggest mistakes we can make in analytics is looking for data after we have sourced all our ideas. For example, in marketing, where creative minds brainstorm key ideas, campaigns and strategies in various channels, they may ignore data before consulting. Instead of ignoring data from some sources, we must build a new mindset where insights from data emerging from different sources help define ideas, strategies and the course of action. This, however, does not mean that we should follow whatever data is telling us blindly. Rather, our natural creativity should be complemented by analytics.

2. Choosing the wrong visual

One of the advantages of business intelligence is the push toward data visualization. Visuals are easily understandable compared to tables and numbers. However, as we develop analytics, we must strike a balance and design insights from consumable and actionable data while ensuring that the data is understandable. While tables are good, there will be no progress if we continue presenting analytics in the form of raw data tables. This is even more complex when the people we are targeting are knowledge workers who may have knowledge of the insights but are not accustomed to the traditional analysts’ language.  

3. Performing analytics without objectives

A lack of a properly defined set of objectives leads to struggling in data analytics. Therefore, before you can even think of diving into your data, ensure that you have gone through a structured process to identify the business objectives you need in your campaign to accomplish results. The business objectives need to be translated into a quantifiable set of primary key performance indicators that can clearly evaluate the successes and failures. Furthermore, you should ensure there is coverage across the entire customer journey and lifecycle. This will provide a framework that will align your analysis with the business outcomes.

4. Over-reliance on data

Although data is crucial in modern businesses and decision-making, over-relying on it can be counterproductive in most cases. This is becoming a big concern to many professionals as they try to make informed decisions based on data. Accordingly, current data models are not always responsible for responsible. Therefore removing people from decision-making roles can have adverse effects. With the increasing customer expectations, insights have become critical in empowering humans and increasing their expertise in decision-making. However, this does not mean that you should only depend on data. Rather, you need to strike a balance between data and human knowledge.

5. Focusing on the wrong insights

While data can be useful in making decisions, insights can sometimes mislead and can also distract, even if the data is accurate. This is caused by too much data that is generated from many sources. Therefore, focusing on the wrong insights can be detrimental to decision-making as the decision arrived at can be wrong. Therefore, there is more reason to focus on how people make decisions. While we may concentrate on improving metrics, we can sometimes lose sight of the impact of the metrics and what they were supposed to achieve.

Ever since the invention of computers many developments have shaped human lives. The invention of the internet was a landmark achievement which set up the stage for more things that followed. Many would have thought that the internet was the biggest thing ever but it was only a lead-in to developments in the world of big data, AI and IoT.  Big data, AI and IoT have revolutionized the world we live in but what exactly are these terms?

AI, IoT, and big data are among the most talked about topics but still highly misunderstood. The tech jargons has been difficult to grasp for non-tech people but this article sheds a little light on the difference between the three terms, how they are related and how they differ.

The advent of social media and e-commerce led by Facebook and Amazon respectively shook the existing infrastructure. It also altered the general view of data. Businesses took advantage of this phenomenon by analyzing social media behavior through the available data and using it to sell products. Companies began collecting large volumes of data, systematically extracting information and analyzing it to discover customer trends. The word big data then became appropriate because the amount of data was orders of magnitude more than what had previously been saved. Basically, big data are extremely large sets of data which can be analyzed to reveal patterns, associations, and trends by using specialized programs. The main aim of doing so is to reveal people’s behavior and interactions, generally for commercial purposes.

Once the concept of big data had settled in and the cloud became a convenient and economical   solution for storage of huge volumes of data companies wanted to analyze it more quickly and extract value. They needed to have an automated approach for analyzing and sorting data and making decisions based on accurate information by businesses.

To achieve this, algorithms were developed to analyze data which can then be used to make more accurate predictions on which to base decisions.

Cloud’s ability to enable storage coupled with the development of AI algorithms that could predict patterns of data, meant that more data became a necessity and so was the need for systems to communicate with each other. Data became more useful as AI systems began to learn and make predictions.

The internet of things (IoT) is a collection of devices fitted with sensors that collect data and send it to storage facilities. That data is then leveraged to teach AI systems to make predictions These concepts are now making way into our homes as smart homes, smart cars, and smartwatches which are in common use..

In short, big data, AI and IoT are interrelated and feed off each other. They depend on each other for operations as AI uses the data generated by IoT. On the other hand, huge datasets would be meaningless without proper methods of collection and analysis. So yes, big data, IoT and AI are related.

The concept of big data has been around for a number of years. However, businesses now make use of big data analytics to uncover trends and gain insights for immediate actions. Big Data Analytics are complex processes involved in examining large and varied data set to uncover information such as unknown correlations, market trends, hidden patterns, and customer’s preferences in order to make informed business decisions.

It is a form of advanced analytics that involves applications with elements such as statistical algorithms powered by high-performance analytics systems.

Why Companies Use Big Data Analytics

From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, big data analytics which is driven by analytical software and systems offers benefits to many organizations.

• Analyze Structured Transaction data: Big data allows data scientists, statisticians, and other analytics professionals to analyze the growing volume of structured transaction data such as social media contents, text from customer email, survey responses, web server logs, mobile phone records and machine data captured by sensors connected to the internet of things. Examining these types of data help to uncover hidden patterns and give insight to make better business decisions.
• Boost Customer Acquisition and Retention: In every organization customers are the most important assets; no business can be successful without establishing a solid customer base. The use of big data analytics helps businesses discover customers’ related patterns and trends; this is important because customers’ behaviors can indicate loyalty. With big data analytics in place, a business has the ability to derive critical behavioral insights it needs to retain uts customer base. A typical example of a company that makes use of big data analytics in driving client retention is Coca-Cola which strengthened its data strategy in 2015 by building a digital-led loyalty program.
• Big Data Analytics offers Marketing Insights: In addition, big data analytics helps to change how business operates by matching customer expectation, ensuring that marketing campaigns are powerful, and changing the company's product line. It also provides insight to help organizations create a more targeted and personalized campaign which implies that businesses can save money and enhance efficiency. A typical example of a brand making use of big data analytics for marketing insight is Netflix. With over 100 million subscribers; the company collects data which is the key to achieving the industry status Netflix boasts.
• Ensures Efficient Risk Management: Any business that wants to survive in the present business environment and remain profitable must be able to foresee potential risks and mitigate them before they become critical. Big data analytics helps organizations develop risk management solutions that allow businesses to quantify and model risks they face daily. It also provides the ability to help a business achieve smarter risk mitigation strategies and make better decisions.
• Get a better understanding of their competitors: For every business knowing your competitors is vital to succeeding and growing. Big data algorithms help organizations get a better understanding of their competitors, know recent price changes, make new product changes, and discover the right time to adjust their product prices.

Finally, enterprises are understanding the benefits of making use of big data analytics in simplifying processes. From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, the implementation of big data analytics can help businesses gain competitive advantages while driving customer retention.

Big data is fast gaining momentum, and so are the big data projects. Companies are increasing in size and ambition. However, the rising number of big data projects does not mean that they all succeed. Gartner estimates that the number of big data projects that fail is about 60 percent in 2016. In 2017, Gartner revised the number of big data projects that fail to be about 85 percent, which was even higher than the rate stated before. Nothing has changed since then. Even in 2021, the rate of failure still ranges at around 80 percent. Here are some of the reasons why big data projects fail.

1. Poor integration

Siloed data is a leading technological problem that causes big data failures. Since data is stored in multiple sources, integrating it into one and using it to get insights that a company needs is a big challenge. This is even bigger problem if legacy systems are involved. It costs a lot of money and often does not result in the desired outcome. According to Alan Morrison of PwC, siloes create data lakes that are just data swamps. Organizations can only access a small percentage of data with little relationships that are inadequate to find patterns and get enough knowledge. Without a graph layer that interprets all instances of data mapped underneath, you have a data lake that is a data swamp.

2. Not defining goals

Like any other project, big data projects require a proper definition of goals and objectives. Sadly, most people who undertake big data projects do not set goals that they need to achieve. Most of them think they can simply connect the structured and unstructured data and get the insight they need. As a project manager, you need to define the problem and develop the goals you want to attain. Having a clear definition of the problem and defining it in time helps achieve the desired goals accurately. However, many big data project leaders lack vision. This ends up confusing the company on big data projects and its desired objectives.

3. Shortage of skills

There has been a widespread shortage of talent in the data science industry over the past few years.  A 2018 report by LinkedIn reported a shortage of more than 150,000 individuals with data science skills. These are people such as data engineers, mathematicians, data analysts, and others. Since the field is in its initial stages, it is often hard to get people with the required skills. This slows production and ends up stalling the well-intentioned big data initiatives. Additionally, many enterprises cannot run several projects simultaneously without the right skills because they lack enough personnel.

4. Lack of transparency

Lack of transparency in big data projects can result in a disconnect between technical and business teams. For instance, while the data science teams usually focus on the accuracy of models that is often simple to measure, business teams, on the other hand, are concerned mainly with metrics like business insights, profits/financial benefits, and interoperability of the final model produced. The lack of clarity and proper alignment between the teams leads to the failure of big data projects as the different teams try to measure different metrics. This is made worse by the traditional data science initiatives that use blackbox models that lack accountability and are hard to interpret, making it difficult to scale.

The above reasons for the failure of big data projects indicate the need for proper plans when implementing big data projects. The problems can be sorted by planning ahead, working together, and setting realistic goals.

According to reports, the global big data as a service (BDaaS) industry is expected to grow significantly in the coming years. The sector was valued at $4.99 billion in 2018 but will likely reach more than$61 billion by 2026. This growth is attributed to the fast adoption of big data as a service in different industries. Other factors that are expected to drive the BDaaS industry are the rising demand for actionable insights and the increasing organizational data across businesses due to the digitization and automation of most business processes. Here are trends that you should expect in the BDaaS industry:

• The increased adoption of BDaaS by social media platforms will lead to growth

The increase in digitization and automation of business processes is the leading factor in the adoption of BDaaS and its subsequent market growth. With the ongoing deployment of the 5G infrastructure, this demand will become rapid as social media platforms such as Snapchat, Instagram, Twitter, Facebook, and YouTube, among others, embrace data as the main approach to reaching customers for growth. Consequently, social media platforms will play a crucial role in the rising global BDaaS market.

• Big companies will hold the largest share

Large multinationals continue to lead in the adoption of BDaaS solutions. With competition heating up, they are likely to continue investing in these solutions as they seek to access customer data and gather the right insights for improved decision-making. They help collect data scattered in various locations or departments to gain valuable insights through big data analysis. Large corporations are spending large amounts of money on training their employees and leveraging the benefits of BDaaS solutions as they seek to edge their competitors and know exactly what their customers want.

• Hadoop will continue in its leadership in this area

In the last year, Hadoop was a significant player in big data as a service. The Hadoop-as-a-service segment held about 31.6%, with the rest sharing the remaining 68.4%. Moving forward, this Hadoop segment is expected to grow exponentially, gaining more CAGR in the future as the craze for BDaaS continues rising. The growth will result from the continued adoption of Hadoop-as-a-service solutions among the small and medium-sized companies (SMEs) worldwide who seek to take advantage of this technology in their service provision. 

• North America will continue dominating BDaaS investments

In 2020, North America was leading in BDaaS investments with $ 6.33 billion. This region is expected to continue holding the leadership spot between now and 2026 in terms of adopting big data as a service and the revenue coming from this industry. This is due to the number of significant players that will invest in it and others such as Intel Corporation that will go on manufacturing chips that will help in the expansion of the existing storage. However, the Asia Pacific region will register a significant increase as countries such as India, China, Japan, and South Korea raise their investments.

• Large companies will embrace joint ventures to strengthen their positions in the market

Large companies that have a global presence are looking for better alternatives to stay ahead in the competition. One of the strategies includes mergers, acquisitions, partnerships, and joint ventures. In most cases, smaller companies are acquired by bigger ones, while others may strike partnership deals to compete favorably in the market. IBM is one of the companies with large big data as a service market share and has been launching solutions and building partnerships that help companies gather data of customers for use in marketing and decision-making activities.

While the coronavirus pandemic has left the world bleeding, it has also highlighted weaknesses in the global healthcare systems that were hidden before. It is evident from the response to the pandemic that there was no plan in place on how to treat an unknown infectious disease like Covid_19. Despite the challenges that the world is facing, there is hope in big data and big data analytics. Big data has changed how data management and analysis is carried out in healthcare. Healthcare data analytics is capable of reducing the costs of treatment and can also help in the prediction of epidemics’ outbreak, prevent diseases, and enhance the quality of life.

Just like businesses, healthcare facilities collect massive amounts of data from patients during their hospital visits. As such, health professionals are looking for ways in which data collected can be analyzed and used to make informed decisions about specific aspects. According to the International Data Corporation report, big data is expected to grow faster in healthcare compared to other industries such as manufacturing, media, and financial services. The report estimates that healthcare data will experience a compound annual growth of 36% by 2025.

Here are some ways in that big data will make a difference in hospitals.

1. Healthcare tracking

Along with the internet of things, big data and analytics are changing how hospitals and healthcare providers can track different user statistics and vitals. Apart from using data from wearables, that can detect the vitals of the patients, such as sleep patterns, heart rate, and exercise, there are new applications that monitor and collect data on blood pressure, glucose, and pulse, among others. The collection of such data will allow hospitals to keep people out of wards as they can manage their ailments by checking their vitals remotely.

2. Reduce the cost of healthcare

Big data has come just at the right time when the cost of healthcare appears to be out of reach of many people. It is promising to save costs for hospitals and patients who fund most of these operations. With predictive analytics, hospitals can predict admission rates and help staff in ward allocation. This reduces the cost of investment incurred by healthcare facilities and enables maximum utilization of the investment. With wearables and health trackers, patients will be saved from unnecessary hospital visits, and admissions, since doctors can easily track their progress from their homes and data collected, can be used to make decisions and prescriptions.

3. Preventing human errors

It is in records that medical professionals often prescribe the wrong medication to patients by mistake. These errors have, in some instances, led to deaths that would have been prevented if there were proper data. These errors can be reduced or prevented by big data, that can be leveraged in the analysis of patient data and prescription of medication. Big data can be used to corroborate and flag a specific medication that has adverse side effects or flag prescription mistake and save a life.

4. Assisting in high-risk patients

Digitization of hospital records creates comprehensive data that can be accessed to understand the patterns of a particular group of patients. These patterns can help in the identification of patients that visit a hospital repeatedly and understand their health issues. This will help doctors identify methods of helping such patients accurately and gain insight for corrective measures, that will reduce their regular visits.

Big data offers obvious advantages to global healthcare. Although many hospitals have not fully capitalized on the advantages brought about by this technology, the truth is that using it will increase efficiency in the provision of healthcare services.

Big data integration is a critical step in any Big Data project. However, some challenges and issues must be taken into account while integrating data. With the growing number of data consumers, big data integration can become a problem that any company needs to respond to. Although it may sound easy, big data integration is not simple as it sounds because large data sets that are structured, unstructured and semi-structured are involved. All these diverse data sets are to be stored in a data warehouse for later retrieval. Some of the challenges encountered during data integration include uncertainty in the management of data, synchronization across data sources, availability of skills and getting the right insights. Despite these challenges, managing integrated big data makes decision-making accurate and ensures the decisions arrived at are insightful.

Big data integration tools

As big data continue being appreciated across different industries, the tools for integrating big data should continue being reevaluated to identify their abilities to process ever-increasing unstructured data. Data integration technologies should have a common platform that supports data quality and profiling.

Big data integration challenges

1. Finding the personnel

With the rising adoption of big data, data scientists and analysts continue to be in high demand. There is a lack of individuals to fill the vacant positions in the big data research industry. While a typical big data expert must have experience with various big data integration tools and an understanding of data organization, coming across such people is never easy.

2. Extracting data

The process of bringing in data that come from different sources is a massive challenge that needs to be addressed appropriately. With the many sources and diversity of data, the skills required to navigate the process of extraction are needed to analyze and process it to help in decision-making.

3. Synchronizing data from different sources

After data from different sources has been extracted, it must be synchronized. This data uses different schedules and rates and can be desynchronized from the source. Synchronization provides consistency in systems while continually updating. With the traditional data management systems, extracting data migrating and transforming it promotes desynchronization. Therefore, synchronizing it will minimize variations in data.

4. Choosing the right strategy

Big data integration mostly starts with the need for information to be shared. This can be followed by the interest in breaking down the existing data silos to allow data to be analyzed. The biggest challenge for many businesses is that they often jump from one project to another without laying down an organizational plan. Therefore, a true data integration plan must be developed complete with security and compliance to meet the goals that can sometimes be difficult to achieve.

5. Security issues

Data is a new goldmine, and hackers know this quite well. Therefore, companies and data users must always ensure that big data integration is secure. Sadly, most organizations do not understand the sensitivity of data and the security challenges. Securing data can also face problems because the data sources are diverse, and data breaches can occur. Therefore, integrating data and storing them safely needs to be a key priority.

6. Demand for skilled analysts

With the rising adoption of big data and analytics across industries, there has been a rising demand for top big data and analytics professionals across the globe. The scarcity of analysts and data engineers who are the key drivers of big data projects have made big data integration difficult. Therefore, companies that intend to deploy data integration must be aware of these key challenges and try as much as they can to address them for success in their projects.

Big data analytics is the leading technology that most modern organizations are venturing into. Without this technology, most companies are blind and deaf and cannot take advantage of the massive amounts of data available in the connected world. Using big data in marketing helps uncover valuable information about the customers, allowing you to connect with them at a personal level. Unlike a few years ago, current businesses do not need to rely on shoddy market research companies to gain insights. Rather, they dig into their own datasets, get past superficial metrics such as location, age and gender of the customers, and uncover valuable information about different demographics. Under the traditional datasets, this was difficult and unattainable until recently.

Regardless of what you are trying to do in your organization, big data in marketing has proven to be one of the most important tools that any organization can rely on. It is useful in improving customer loyalty, enhancing the performance of an organization and making pricing decisions, all of which are important in marketing. These are aspects of big data, which include not only the analytics but also data ingestion, storage and integration and others, all of which are necessary to improve marketing. With big data and its related technologies, data can be filtered, curated, processed, and analyzed in vast amounts gathered during transactions.

Finding new leads

One of the key benefits of big data analytics is that it can help an organization gain insights regarding how users feel about a particular product or service that you offer. With this data, you can easily identify services or products frequently purchased. With this data at hand, you can link it to sites such as social media platforms where you can identify challenges that customers face in that particular product or service, tap into the new markets using these insights and gain access to an even bigger audience. This offers your business new leads that increase social selling.

Generating new leads

Data gathered from social networks can also be used in recommender systems. For example, Amazon has a recommender system that creates a customized homepage for each client based on their profile and history of interaction. This is good in generating repeat sales, therefore, increasing overall sales. Such is possible if the record of sales of every user is kept in a database. Since various tools in the market are available for such a job, reviewing sales reports has become easy. Marketers do not need to overburden themselves by using spreadsheets that need regular updates. 

Improved customer acquisition

Enhanced customer acquisition is another key benefit of big data to marketing. According to a McKinsey survey, intensive users of customer analytics were found to be 23 times more likely to outperform their competitors in customer acquisition. Therefore, the cloud allows organizations to collect and analyze personalized data from different sources like web, mobile application data, emails, live chats and in-store interactions.

Although using big data in marketing efforts has many advantages, challenges need to be surmounted to achieve efficiency. Some of the problems that marketers encounter include disparate data systems, which cause a disconnect, making customer personalization ineffective, lack of cross-department collaboration, and poor quality of streaming data sources.

As a marketer, the first step in big data marketing is to integrate data from different sources. Once you use big data analytics, you will understand your customers much better. This will ease your ability to connect with them with relevance and help them turn interactions into conversations with ease. Therefore, big data will play a critical role if you want to grow your small business into a larger one.

For any business, minimizing time to insight is a significant cost-saving driver. This is the reason for the increased adoption and investments in big data. As competition in different industries hots up, organizations increasingly feel the need to learn quickly and act faster. Survey indicates that 83.5% of survey respondents view factors related to speed, insight, and business agility as the main reasons for increased investment in big data. According to research, customers would be reluctant to shop with a given retailer if they receive poor customer service and a bad experience. This is revenue lost, a potential increase in shipping costs, and a risk of product returns that may never be resolved. Here are some ways you can prepare to boost your returns with big data in your e-commerce business.

1. Use e-commerce returns data to improve your business

Unlike brick-and-mortar stores, e-commerce retailers have no luxury of physical interaction with customers. This has a significant effect on product returns for e-commerce retailers, which are always higher. To mitigate this challenge and reduce the costs caused by product returns, Shopify returns management software, which automates and eases the return process, is now a proper solution for many online retailers. Although there are various solutions that help save time, most of them do not give retailers the necessary insights needed to improve their products or websites.

With big data, retailers can identify the challenges in their websites, such as misleading product photos, inaccurate product descriptions, inconsistencies in sizing conventions, and how often they drive product returns. Get feedback from shoppers concerning the problems they encounter and the leading causes of returns. This information can help improve products. Use the data to align the shoppers’ expectations and reality. Identify why certain products have high returns and spot issues by looking at the frequently used words.

2. Use the returns data to learn about your shoppers

One of the biggest challenges that retailers face in e-commerce is knowing their shoppers' interests, purchase patterns, and challenges. Although returns may have implications on the cost of running a business, it is not entirely a bad thing. It shows that a customer was close to getting what they needed. You can learn a lot about the needs of customers and their relationship with your brand without ever meeting them. The comments from customers are valuable in improving customers’ experience. Comments about returns give unique positive and negative feedback that can offer insight into the shortcomings of a product and shine a light on issues that may otherwise go unnoticed. Big data helps retailers identify acquisition and retention, which are critical for the survival of the e-commerce retail business. With big data, you learn when the customers are likely to repurchase, how customers have changed over time, and revenue coming from return customers, among other critical things necessary to run a business.

3. Use e-commerce returns data to evaluate and improve operations

The operational processes in retail have a substantial impact on customer experience. Proper operational processes enhance efficiency and allow products you order to arrive in time. For instance, if a customer orders a swimsuit for a pool party event, it should arrive in time for use in that event. If the product arrives late and the client does not receive a refund for several weeks, it would have a negative experience for the customer who will never shop on that website again. With such problematic processes, you will never compete in today’s business landscape. Blend sales and returns data to help improve your processes both in the forward supply chain and reverse supply chain. This is the only way you can compete in the modern retail landscape.

The holiday season is here again, and you do not need a crystal ball to know that the year’s biggest shopping season is here with us. According to a survey, November and December account for about half of the annual sales for many retailers. Although you might have been successful in the past holiday seasons, every holiday season is unique and often comes with its share of unique challenges. We bring fresh insights for better holiday shopping results to help you deal with this year’s challenges. With these insights, retailers are informed and inspired, and the shoppers are empowered. 

1. Online sales will continue rising

The COVID-19 pandemic has increased online sales on a scale never witnessed before. This was evident in 2020, which broke records. This year, you should expect online sales to continue growing for about 10% in the US and around 7% in the rest of the world. As we inch closer to November and December, known for increased shopping activity, online shoppers in the US will spend over $259 billion and about $1.2 trillion globally. This means that you need to have an excellent online presence as a retailer.

2. Gift cards will continue being a leading choice for consumers

Gift cards still rule the shopping world, and you are likely to get a few in the coming season. Therefore, as the new season draws closer, prepare gift cards for your customers because it is still one of the favorite discount options. Whether you are running a department store or a grocery store, gift cards might work well for your customers.

3. Rising costs will affect the shopping experience

There has been an increase in the cost of doing business across the world. This has led to the rise in labor costs and the costs associated with freight and manufacturing. As the holiday season gets closer, consumers will pay 20% more for their shopping as retailers and manufacturers raise the cost of products. This will likely affect shopping this holiday season as most people scale down their shopping.

4. There will be more swapping and shopping post-holiday

After the holidays, some people would expect a reduction in shopping activity. However, this is not always the case. Expect more shopping and swapping to go on even after the holidays as consumers plan to cash in on after-holiday bargains where the cost of products has dropped. Most consumers also expect to return gifts after the holidays, meaning shopping will continue some days after the holidays have ended.

5. Social media is still king

Social media continues to rule the marketing world, and this will not be different in the coming holiday season. As holidays get nearer, many retail companies will use hashtags to drive their sales messaging about their products. As retailers take advantage of social media platforms for marketing, you will see different hashtags. The only thing that retailers need to do is be consistent and look at the trends and what customers love. Companies will embrace concepts such as Facebook Live video to market their products.

Generally, with the pandemic still in place, there are still many challenges that make this year’s holiday experience different than ever before. Although in-store shopping will continue, online shopping is gaining momentum, and many retailers will continue embracing it. Millennial shoppers are most likely to increase their shopping and spend more money during the holiday season. On the contrary, the older shoppers tend to stick with their budget, although they will spend more compared to last year where the pandemic was at its peak. Regardless of the differences between this year’s shopping habits compared to those of the other years, you must have a concrete strategy to appeal to your customers.