In order to get the maximum out of technology, businesses are adopting data analytics trends

The power of data and analytics is no longer hidden. Today businesses of all sizes, starting from small to medium and big are availing data analytics in their routine to streamline operations. Without data analytics, companies are blind and deaf. Data analytics allows businesses to understand the market and their customers’ preferences and suggests solutions that could yield big profits. A rough estimation suggests that data analytics in business will increase five-fold by 2024 because of the rapid rise in technology adoption. Once upon a time, data analytics was confined to the tech industry. Only IT professionals, data engineers, and top-level enterprise executives got their hands on the technology. But things changed when laymen started embracing artificial intelligence. Today, big data, machine learning, cloud computing, data analytics, and many more technologies are a part of our everyday life. Many companies unveil data analytics in business to optimize business processes, cut costs, increase revenue, improve competitiveness, and accelerate innovation. In order to get the maximum out of technology, businesses should adopt recent data analytics trends. Data analytics trends such as decision intelligence, edge computing, data storytelling, etc are unraveling a world where businesses can understand their customers and address their needs like never before. In this article, Analytics Insight takes you through some of the top data analytics trends that businesses should follow in 2021.

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Top Data Analytics Trends for Business

Moving to Scalable AI

Post the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chain.

Decision Intelligence as the Powerhouse of Decision Making

In modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable data.

Augmented Data Management to Shorten Data Delivery Time

The next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their architecture.

Edge Data and Analytics at the Core of Operations

The inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.

The Stronghold of the Cloud Continues

Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2022. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track trials.

No more Big Data, Let’s go to Small and Wide Data

For almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.

Automation at its Best

Business outcomes rely on data. But over the past few years, big data is getting more complex. For example, the inflow of data is in various forms like videos, images, documents, texts, files, etc. Besides, there are also two other categories called structured and unstructured data, which makes data processing even more hectic. The only way out of this is by automating the process of data discovery, preparation, and blending of disparate data. Besides, automating the data discovery and analysis process helps analysts focus on high-value-added activities.

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Amid the constant disruption from unlikely competitors and changes in the industry occurring in faster and shorter cycles, time to market is constantly shrinking. To run a business and navigating this complexity in the present day and age, managers need relevant information and insights that can help understand the intended target audience, and their needs and preferences.

Thanks to the availability of multiple sources of market and customer data, analytics and artificial intelligence can be used to effectively respond to market dynamics, and drive revenue, profitability, and customer satisfaction. These new technologies are no longer a privilege for tech firms. An increasing number of companies are leveraging these tools to steer through unsettled waters and enhance their performance.

A few years ago, AI technology was being mostly used by early adopters. Any new technology typically faces a “chasm” in going from early adopters to the majority. With the pandemic and the inevitability of transformation, AI technology has “flown” over this chasm, entered the mainstream, and is now getting industrialised within companies.

There are several ways in which businesses can use AI and analytics to spur growth:

Customer monetisation

Analytics can be extensively leveraged to personalise the customer experience. The most optimum products and services can be offered at the right price and the experience can be optimised to the individual customers’ liking.

The impact of this work is amplified in the digital domain. Every interaction is being recorded which generates massive amounts of data, and this can be used to personalise the experience in real-time. All this leads to higher customer satisfaction and maximisation of revenue.

Optimised marketing spends

Companies spend a lot of money on marketing through various means and channels. It has been often said by CMOs – “I waste half the money I spend on advertising, I just don’t know which half.” Analytics and machine learning models can assess the marketing spend across channels to identify the optimum mix to drive revenue and brand equity. This can be fine-tuned by various customer segments and types.

Competitive advantage

Enterprises can collate data from within their organisation and the industry to have an upper hand in understanding the competition and market trends. By combining the information generated, organisations can get constant insights into sales, potential gaps in the market, and product improvement.

These insights enable the teams to work in collaboration, provide real-time responses to competitive tactics, and achieve better outcomes.

Optimisation of the supply chain

Analytics can be used to ensure that supply keeps up with the business with optimised costs, especially with the demands of digital business models which need short-time deliveries to customers.

The future of AI

It is impossible to anticipate every use case of AI in the future. Just like it happened with the internet, AI-based innovation will throw up use cases that we cannot fathom today.

Businesses will be able to leverage AI to answer complex questions around growth opportunities like new markets and product lines, and make multifarious decisions that are scientific and rooted in data.

Some exciting use cases are where the realm of AI is going beyond structured data to understand and analyse all sorts of unstructured data like images, audio, text, and video.

Using these techniques, AI is now even being used to optimise creativity and to help marketers decide what kind of creativity will appeal to specific audiences for specific campaign objectives.

However, the one key area where AI technology will impact the most is in disrupting entire industries and creating new business models. For example, what Tesla has done to the auto industry and what Netflix has done to the entertainment industry.

AI has a huge scope of disruption and transformation in areas like healthcare and education, and many others. All this is going to lead to transformational business opportunities for existing companies and new entrepreneurs.

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Data analytics is only for IT people, right? Wrong!!

Recently I was working for a CFO of a drinks company who couldn’t master anything to do with data or Excel. Problem was, all of his figures were produced in Excel by his staff (who didn’t speak English- or at least pretended not to). Obviously, everyone was cheating. But he had to sign off the accounts, or face fines… but if he signed off the wrong accounts, he could face – prison?

But you might be thinking that you cannot possibly get into data analytics now because you don’t have time. I get you. I’ve been living in Vietnam for five years and, although, it would be much better experience if I spoke Vietnamese, I don’t. So, what do you do? Well, you get dashboards. Ok, ok, but consulting companies are always trying to sell you dashboards for lots of money, and since you don’t know much about data analytics, you aren’t sure who is ripping you off more.

So, a little time spent understanding the basics can help you to choose the right vendors, understand the solutions that are necessary and be aware of the risks that you are facing every day.

Let me let you into a little secret…

Most top management out there, over 40, pushing 50, are not really aware about what data analytics means.

A while back, I was trying to persuade the head of internal audit from a leading retail group about data analytics and data visualization.

But there was a disconnect.

Despite the fact that we had successfully completed another of his projects for him in the past, he could not understand why he would need data analytics when he already has detailed supplier balances that he can review from SAP.

Furthermore, he was having a hard time to trying to convince his audit managers why they should change from sampling to data analytics.

There is a lot of hype around data analytics, but the truth is, 60% of the internal auditors out there still have not grasped the reason why they should be done, or how they can provide benefit.

But you don’t need to worry. You are not alone. 

Even in the Big 4 audit firm where I used to work ten years ago, we hit a roadblock. The partners in the audit firm just didn’t want to let us do more data analytics. Maybe that’s a natural resistance to change. Maybe it’s also because they are not aware of all of the benefits that data analytics brings.

This article is aimed at helping you to understand how data analytics does actually helps you in many ways to get your work done quicker and in a more interesting and impactful way than ever before. In reality, data analytics changes everything.

You may be thinking, “I’ve heard this before”…

… consulting companies are always trying to sell me data analytics software.

And you are right. Those huge consulting companies will try and sell you data analytics services and software and unbelievable prices, despite the fact that they have already done the same thing, hundreds of times before. They would rather copy-paste the information to you, or, despite the fact that they don’t actually have the skills available, they will outsource it to India, but still give you a very high price tag and an extremely long delivery time.

If anyone is trying to sell you data analytics for SAP, and they reckon that the time delay for delivery is over one month, then you should be concerned.

So, is that because data analytics is easy? Not really.

But it is because data analytics has actually been around for a long time, and like in our company, since we have been making data analytics dashboards for SAP for over 10 years, we already have hundreds of them ready to deploy and we have already optimized the configuration process.

But, you say, “if I get some pre-designed dashboards, that is not going to be exactly what I want”. “It’s not going to be relevant to our company”. This is true. You are totally right. This is why it is also great if you can get to a stage where you can understand the data analytics process and then be able to communicate with those making your dashboards so that they can tweak them in the right direction.

So, what do you need to know, if you are new to data analytics and you feel like a “Data Analytics Dummy”? 

Here are some pointers:

1. The data that we are analysing, usually comes from the ERP system, thats the “Enterprise Resource Planning” system. This system is typically SAP (but sometimes companies give it an internal name, such as “Symphony”.

2. End-users will enter transactions (such as purchase requests, purchase orders, logs of goods receipts, invoices, payments, etc), into the SAP system.

3. When an end-user enters a transaction, this transaction will get input into a database.

4. A database, is a collection of tables. A table is basically a list. Like an Excel list. For example, in your SAP system, you have a list of purchase orders, a list of goods receipts, a list of invoices, a list of payments.

5. In order to make sure that your SAP system works quickly and efficiently the data will be organized in the tables in a very clever, way, known as a “Relational Database”. A relational database has key aspects:

• Each table has keys that help it to link to other tables, for easy lookup of information. For example, if you have a list of purchase orders in one table, and a list of supplier information in another table, there will be a key in each of those tables (in this case the supplier number), that links the two together.
• Normally each table will have one key that is known as the “primary key”. This key is usually unique, within the table. Meaning that it only occurs once. For example, for the central list of suppliers in SAP, this unique key is the supplier number.
• Some tables’ primary key is actually more than one piece of information: for example, the primary key for purchase orders is the purchase order number and the purchase order line item, used together. That’s two pieces of information, or two columns in the table, that make up the unique primary key.
• ​In order to save disk-space, the tables will also be organized in such as way, so as to minimize the repetition of information. Typically, this means that they will be organized into “header” information and “detailed” information. For example, the list of purchase orders, is not in one table, but in two. The header purchase order table, will contain information, such as “date”, “user that entered the purchase order”. This table’s primary key will only be the purchase order number, because this table will not contain any line-item detail. The second table for purchase orders is the purchase order line items. This table will contain the amount per line item, the material number for each line item, etc. The primary key of the second table will be two columns joined together: the purchase order number and the purchase order line item.

7. The information in your business is grouped into different modules. For example, the purchase orders, goods receipts information in SAP, is organized into the MM module (or Materials Management) module, whereas the sales orders and deliveries information is grouped together into the SD module or (Sales and Distribution) module.

8. In SAP, as with any ERP system, the data entered in the different modules, will flow through to the final module, which is the FI module (or the Finance module). This module is like the keystone of your entire SAP system. It is from the FI module that you will print out financial reports, such as the Balance Sheet, the P&L, etc. The FI module is so important, because we can visualize the flow from all the different modules into the FI module, in order to see where any gaps occur along the way. For example, if some enters a purchase order, this purchase order will flow through to a goods receipt, and this goods receipt will flow through to an invoice and this invoice will flow through to a payment. And all of those values should match each other (unless your company allows for a threshold difference).

9. So, any data analyst will always tell you, or should always tell you, that they have started off their project, by comparing the total values of the data, with that on the financial reports for your company.

10. But hey, let’s not stop there. What is the real key point of validation? What really gives us 100% confidence that all of our transactions are flowing correctly, that no money has gone astray along the way, that the financial reports are correct? Well, as a CFO once explained to me about 12 years ago, the only key way, to be 100% sure that the data in your company is correct, is to compare it to the bank statement. That’s right, the bank statement (which is at the bank), not in SAP, is the best way to check if your financial statements are correct. The first thing to do, is a line-by-line comparison between your bank statement and your General Ledger.

11. What is a General Ledger? I hear you say. Let’s look at this diagram to help us:

In the diagram, you can see that a sales document on the left, will flow through to the accounting document on the right. The accounting document gets put into a log, which is also known as the “General Ledger”.

The “General Ledger” is kinda like the log of everything, including the log of cash, that goes in and out of the bank. When someone makes a supplier payment, it is logged in the general ledger (or at least should be!). When a customer pays something, it is logged in the general ledger (or again, should be!).

12. But what do we mean by line-by-line. Well, this brings us back full circle to the beginning. I’m still discussing with that head of internal audit in one of the world’s largest retail groups. And he still is not understanding why he needs data analytics. Ok, so why not?

Well, because his managers are using detailed reports, that show the total balances day-by-day for their suppliers. Ok. But, I say to him, within those totals, what if there is a transaction that is hiding. For example, imagine that you owe your supplier, that happens to be one of the world’s leading distributors of cosmetics, 10 million USD. Ok, it’s normal, you always owe them about that much, because they are always delivered to you, and you are always paying them after 60 days. Everything is fine from that perspective.

But little did you know, that within that outstanding balance of 10 million, lurks a year-end-rebate transaction. It was entered three years ago. IT means that that friendly supplier should pay you back 1 million USD according to the contract that your purchasing team dutifully negotiated for you. But you don’t notice it. Because it is hidden within the 10 million. It’s like this. Actually, you owe them 11 million, and they owe your 1 million. So the balance is 10 million. So your auditors think, well, we owe them 10 million, which is the same as last month, so everything is good, end of the story. Unfortunately for your company, you don’t get to reclaim your 1 million back from them.

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With humungous data being the reason why organizations function, the importance given to data cannot be merely put into words. Over the years, data has enjoyed prominence in every field that one can possibly think of. This is why everyone dreams of landing a job in this field. However, getting a little confused as to what is data science, big data and data analytics and how are they different from each other is natural. These three terms have utmost importance in the magical world of data. They are similar in certain aspects and different in other areas. That said, having a clear picture in mind regarding all of them would ultimately result in you making a better career choice. Here is everything you need to know about data science, big data and data analytics.

Data science

Data science revolves around filtering the data in a manner that it is possible to extract information and draw meaningful insights from it. This field takes into account both structured as well as unstructured data.

Skills required to become a data scientist

1. Coding languages like R, Python, Java, C/C++, etc.
2. Ability to work with unstructured and structured data.
3. Statistics and mathematics.
4. Understanding the business problem and objective.
5. Problem-solving
6. Critical thinking.
7. Strong communication skills.
8. Fair knowledge about Hadoop and SQL.

Applications of data science

1. One of the biggest applications of data science is in coming up with recommendations to the users based on the history. This is widely used by the E-commerce industry.
2. Digital marketing.

Data analytics

Data analytics is nothing but working on raw data to be able to reach conclusions. This further helps the management in making better decisions. The main objective behind data analytics is to take steps that lead to the growth of the organization. It is solely on the basis of data analytics that the management team decides on new steps to be taken, rejecting certain ideas and even re-working on the decisions already taken. Ultimately, what everything boils down to is – the organization should be in a position to make decisions that address the issues, if any and/or take the organization to a different level altogether.

Skills required to become a data analyst

1. Programming languages are a must to become a data R and Python are the two most sought-after languages by the recruiters.
2. The ability to visualise data.
3. Strong communication skills.
4. Sound knowledge of statistics and mathematics.
5. The ability to convert raw data into a form that it is possible to make better decisions.
6. Machine learning. This is yet another key aspect that one should not neglect when aiming to become a data analyst

Applications of data analytics

Data analytics has a wide range of applications. Some of them are –

1. Gaming
2. Travel and tourism.
3. Healthcare sector, etc.

Big data

The term “big data” evidently throws light on what it could be. Big data refers to huge volumes of data that cannot be processed effectively using traditional methods. The first step starts with processing the raw data that cannot be stored in any of the traditional systems. With data growing manifold, the term big data perfectly fits in. According to Gartner, “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Skills required to become a big data specialist

1. The ability to identify which data is relevant.
2. The ability to create new methods to gather, interpret, and analyze a data
3. Statistical and mathematical skills.
4. Number crunching.
5. Understanding the business objectives.
6. The ability to come up with algorithms to be able to process the data.

Applications of big data

There are numerous applications of big data. Some of the key ones are –

1. Fraud analytics.
2. Telecommunication sector.
3. Customer analytics.

No matter which career path you choose, your career would be promising for the sole reason that data is here to stay! It will continue to play a vital role in our lives for the years to come.

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How artificial intelligence in data analytics can help visualise business data?

In an ultra fast-paced digital world, businesses of all sizes produce huge amounts of data that are challenging to keep up with. Such data carries much promise when it comes to analyzing them. Recent technological advances have changed how enterprise analytics perform. There are still some challenges to using data and analytics in many aspects of an organization. However, when using artificial intelligence in data analytics, businesses can produce outcomes far beyond what they can do manually, both in terms of speed and accuracy.

Analytical approaches comprising predictive models have now begun to shift merely to descriptive approaches, which is already beneficial for many users and continues to be valuable. Descriptive analytics has evolved much, making greater use of visual analytics. Despite this, making use of data and analytics to interpret and envisage significant phenomena in businesses is difficult.

Predictive models capitalize on past data and a reasonable amount of expertise to create and predict outcomes. However, the use of past data here limits how and when they can be deployed. Existing data analytics approaches have historically been a bit narrow. They are focused on particular functions or units, even though many business problems and issues cut across functions and units.

Data Analytics Influenced by Artificial Intelligence

Powered by automation and artificial intelligence, the next-generation of enterprise analytics is emerging. Apart from this, the innovation relies on connections across existing information systems and role-based assumptions about what decisions will be made on data and analytics. AI-enhanced software has the potential to assess data from any source and deliver meaningful insights. It can analyze customer data that can be particularly revelatory and disrupt product development while improving team performance and enabling businesses to know what works and what doesn’t.

Artificial intelligence typically refers to the field of data science. It leverages advanced algorithms to power computers to learn on their own. By integrating AI into their data analytics processes, businesses can be able to automatically clean, evaluate, explain and visualize their data.

In an article, Tom Davenport and Joey Fitts wrote that automation in analytics, often called “smart data discovery” or “augmented analytics”, is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. The systems, in some cases, even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how people analyse and consume data.

Artificial intelligence and automation have made significant advancements in data analytics that were inconceivable a few years ago. Enterprises these days are realizing the benefits of these technologies and using them to examine their data to derive fine-grained insights. AI is now creating new methods for data analysis. Historically, data engineers or analysts have had to use a query or SQL when it comes to analysing data. However, as the significance of data continues to grow, multiple ways to excerpt insights have emerged. Artificial intelligence emerges as a crucial technology, becoming the next step to query or SQL.

Earlier, data and analytics have been discrete resources that needed to be fused to accomplish value. This also required extensive knowledge of what type of data was apt for analysis within an organization. Most data analysts lacked such knowledge in a broader context. However, AI-powered analytics can increasingly provide context. Many key vendors are already using these capabilities in their transactional systems offerings, such as ERP and CRM.

In conclusion, this is just the beginning of data analytics powered by artificial intelligence. As the advances in this technology will continue to evolve, the potential of AI-driven data analytics tools will be striking.

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A machine learning application’s value is dependent on the quality of data used to train and deploy it. Organizations are responsible for creating or acquiring enough data, that this data is useful for the specific application and that the analytics team is capable of sorting through and learning useful things from it.

The knowledge discovery in databases (KDD) finds knowledge in data; organizations use data mining methods to draw out its usefulness.

KDD vs. data mining

While most data scientists are familiar with data mining, KDD is a specialized process that applies high-level, sophisticated data mining techniques to find and interpret patterns from data. Though the terms are sometimes used interchangeably, KDD is used especially for machine learning, databases, pattern matching, AI and enterprise use.

“[In comparison], the term data mining is broadly applied to looking through piles of data and trying to find interesting patterns,” said Peter Aiken, associate professor at Virginia Commonwealth University.

In general, these processes both extract data from large databases, but KDD is more often used to explain the larger picture. There are varying divisions of the steps of KDD but in general they can be broken down into several steps:

Step 1: Selection — Sort out the data you would like to mine.

Step 2: Preprocessing — Data cleaning (removing any noise or outliers within the data set) using statistical techniques or data mining algorithms.

Step 3: Transformation — Data is prepared and developed through dimension reduction and attribute transformation. This step may be quite project-specific but always crucial to the success of the project.

Step 4: Data mining — Outline what kind of data mining would be most useful by judging which objective you are seeking (prediction or description).

Step 5: Interpretation/Evaluation — Assess and interpret the mined patterns, rules, and reliability in comparison to the original objective.

Association rules

Data mining is the process of identifying patterns and establishing relationships by sorting through data sets. Within this broad definition are association rules that analyze the data set for if/then patterns and use support and confidence criteria to locate the most important relationships. Support is how often items appear in the database and confidence is the amount of if/then statements that are correct.

Among the more common data mining parameters include anything from sequence analysis, classification and clustering, as well as forecasting.

Sequence analysis. Identifies patterns where one event points to another, later event.

Classification. Looks for new patterns and can change the way in which the data is organized.

Clustering. Locate and document groups of facts that had not been known yet. Groups are organized by how similar they are to one another.

Forecasting. These parameters within data mining discover patterns in data that point to reasonable predictions.

This is all a relatively manual process, however. Human intervention and decision-making come to play majorly in the KDD/data mining process. This is one of the largest differentiators from a similar process, machine learning. When it comes to machine learning, the quality of data is crucial and data mining allows for better insight to be drawn out from this data.

“Usually the most critical thing in [removing deficiencies in] performance of your model is also usually the most critical step in getting your model put into production,” said Kjell Carlsson, a Forrester Research analyst.

KDD, data mining and machine learning

If an enterprise is working on a machine learning project, then some form of the KDD process is also going on in-house. Both fall under the umbrella of data science and both processes are used for solving complex problems with data.

“The real question is from a user’s perspective, what are you trying to do,” Aiken said. “And if the data that you’re trying to use is more likely to come from a database than a big data pile.”

Machine learning and data mining share the same principles but function differently. A data scientist turns to data mining to pull from existing information to find emerging patterns that can help shape decision-making processes. Machine learning is more active and less hands-on. Machine learning takes this process a step further because it can learn from the existing data and teach itself what to look for in the future and predict patterns. Data mining is typically used as an information source from which a machine learning algorithm can learn.

Both are analytics processes that are good with pattern recognition and are therefore often confused. Machine learning may use some data mining techniques to build its models and data mining can use machine learning techniques to produce more accurate analysis.

“The biggest problem with computer science in today’s environment is that machine learning algorithms don’t have training data,” Aiken said.

Without training data, a machine learning model is unable to reach any kind of effective performance. As Aiken sees it, any boasting about a model without data is like saying well you’ve got this great baseball team you just have to teach them how to play baseball.

Uses of KDD/data mining and machine learning

Data mining and the overall process of KDD have carved out their own specialty. Data mining has been deployed in the retail industry in order to better understand the patterns of customer buying habits. Organizations can mine their customer data for relevant information on the success and failure of items and adjust from there.

It has also been used in finance by organizations looking into potential investments and whether a new organization is going to succeed. Past performance of successful startups, as well as patterns of indicators of business prowess, inform those in the finance industry of where to put their money.

Machine learning’s applications vary widely across industries for purposes such as fraud detection, autonomous vehicles and personalized marketing, among others. Organizations turn to machine learning algorithms to analyze vast amounts of data and provide continued growth and value as more data is brought in.

Machine learning algorithms can function better with relevant data sets and these can be brought about through the process of data mining.

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AI has transformed the nature of the eCommerce business in many ways. Artificial Intelligence or machine intelligence replaces human intelligence with machines that possess the same cognitive functions of learning and problem-solving as humans.

The best part about AI is that it can perform cognitive operations with more accuracy and a faster speed than humans. It is the foremost reason for which eCommerce businesses use AI. From improving customer experience to business market research automation, AI brings about a revolution in the eCommerce industry. Consumer insights is another area in which AI is doing wonders. In this article, we will be discussing four ways in which AI is transforming consumer insights.

Why Is Market Research for eCommerce Essential?

Market research has become essential for eCommerce. From ensuring a better consumer experience to gaining quality consumer insights, market research holds great significance in different spheres of eCommerce businesses. It is impossible to gain relevant and genuine consumer insights without proper market research. Moreover, without accurate consumer insights, you cannot improve customer satisfaction as consumer insights play an essential role in understanding your customers.

Market research helps companies gain competitive and actionable consumer insights that can be used for text analytics and sentimental analysis. It is also the best way to know your target audience and the latest trends in the market. Businesses can generate more leads if they know their consumers better. Moreover, you can also know about your competitor’s strategies with market research. It helps you to learn how you can improve your business. Besides, market research also helps in promotion and advertising.

Earlier traditional market research methods such as surveys and polls were the easily accessible options. However, they are time-consuming and expensive. In the age of digital transformation, AI-driven market research has replaced traditional market research to a great extent. It has resulted in tremendous growth of AI text analytics tools and eCommerce marketing platforms.

Below Are Four Ways AI is Transforming Consumer Insights

Market Research: AI-driven market research for eCommerce helps you get actionable and competitive consumer insights faster. There are eCommerce platforms such as Revuze that use AI tools to gain real-time consumer insights that can be used for sentimental analysis. Earlier surveys and polls were the prominent methods of performing market research. However, they are time-consuming and expensive procedures. Moreover, they are not very reliable. However, market research conducted using AI tools is faster and more accurate. AI tools can be used to extract large amounts of relevant data and user-generated content.

Data Cleaning: Data analytics cannot be performed on inadequate or irrelevant data. Insufficient data comprises missing, irrelevant, duplicate, and inconsistent or data with errors. Performing data analytics on bad data leads to inaccurate results and consumer insights. Therefore, it is crucial to clean the extracted data before running data analytics. However, this process is too time-consuming when done manually. Moreover, it can further lead to human errors. Nonetheless, AI is changing the way companies perform data cleaning. AI algorithms substitute insufficient data with useful data to make it fit for analysis.

Understanding Open-ended Questions: Analysing open-ended questions is not an easy task. Open-ended questions can generate quality insights. However, the main challenge is to analyze open-ended feedback and questions. With a human-centric approach, there are chances of errors and biased analysis. Moreover, it’s time taking and tedious. However, interpreting open-ended questions with AI algorithms is free from any biases and errors. Also, it is less time consuming and less costly.

Faster Insights: With AI, gaining quick consumer insights has been made possible. Surveys and polls are time taking market research methods to gather consumer insights. With the traditional market research methods, taking appropriate actions on the insights takes a long time, and it is not suitable for your business. Whereas, with AI, you can collect actionable and competitive insights in no time.

Faster insights mean that you can act and respond to your customer’s needs in less time. As a result, more customers will be attracted to buy from you. It will, in turn, result in increased sales and profit.

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The coronavirus pandemic has transformed the lives of thousands of employed professionals all across the globe, including the ones in the data science industry. This crisis brought upon a new normal of working from home and pushed analytics to the forefront. Analytics professionals had to alter the way they work to keep up with the times. 

Data analytics and data science professionals who have been working in the industry for a while would understand how the COVID transformation will affect the field. However, a junior-level professional who is just starting out his/her career would experience a different scenario than what they expect. The pandemic might be tormenting for new graduates or amateur data scientists because more companies have no interest in recruiting fresh analytics professionals. That is why it is more important than ever to build your resume with a certification course. Here is how the career in data science will be different post COVID:

1. Increased competition

With virtual hiring and remote working in place, the norms of recruitment have changed. Companies are no longer required to hire talents from their area. This will create more competition, especially for amateur data scientists and freshers who have just begun their careers. Graduates will now be competing with not only the professionals from their own city or country, but also with those living thousands of miles away. However, there is an upside to this. This has also increased opportunities for data science professionals who can now apply for a job outside their country and gain a better salary. 

In order to stay relevant amidst this crisis, it is important for young data science professionals to gain appropriate skill sets and upskill themselves continuously. There are several online edtech companies that offer basic as well as advanced data science and AI courses. By enrolling yourself in one of these programs, you can help yourself sustain during these trying times. The landscape is evolving and businesses are relying heavily on advanced technologies. In order to stay ahead of the curve, it is important for data science professionals to continue learning.

2. Isolated learning process

Upskilling has always been an important aspect of the career of a data scientist. However, now that the pandemic has disrupted how businesses work, many want to hire professionals who have advanced skill sets. So, upskilling has become more important than ever to make advancements in the field. According to a LinkedIn report, 64% of professionals like data scientists have increased their focus on learning during the lockdown. 

However, since the companies have mandated working from homes for their employees, the learning and upskilling process has become isolated. Before COVID, companies offered training programs and in-person workshops for young data scientist professionals so that they could learn the skills needed for the business and get accustomed to the new workplace. The lockdown has omitted the process entirely and professionals have to rely on online programs to learn these skills. Moreover, these data scientists will be working from home for a long time that restricts their communication with their teammates, thus impeding their learning process.

On the other hand, online programs have become the only source for the new data scientists to enhance their skills and gain the knowledge they need to work in the field. It is important that you select the right online course that provides you with practical experience, interaction with the teacher, and other scientists. With the right program, you will be able to take on any challenges you might be facing in the workplace.

3. Increased efforts for collaboration

Data science is one of those fields that require immense collaboration between the team members to solve a problem. Through this effective collaboration, the data scientists are able to help the company enhance its business operations, create better products, and make informed decisions. A data scientist cannot work in isolation and collaboration is what will determine the success of the project.

This collaboration is especially important for new data scientists who have just joined the company. But, thanks to the pandemic, data scientists and analytics professionals are working by collaborating online. But these online collaborations are filled with challenges that require in-person training to understand the problems and business better. These challenges reduce the efficiency and productivity of the data scientists and create a large communication gap between the team members and the supervisors. 

In the field of data science, asking the right questions is important to solve business problems. With online collaboration, amateur professionals will face issues asking the right question at the same time leading to ineffective collaboration and hammering their work. That is why data scientists have to make a lot of effort to communicate in times of online collaboration.

4. Increased contract-based hiring

Another transformation occurring in the workplace post COVID will be contract-based hiring. This is applicable to almost every profession in the world including analytics professionals and data scientists. Once the pandemic cedes, companies will employ cost-cutting measures and hire freelancers, contract-based employees, and gig workers. This will allow them to keep the employer’s tenure for a limited time and avail data science capabilities for a specific project. 

Even though recruiting freelancers or contract-based hiring is beneficial for the businesses post COVID, it will bring added challenges of increased competition. Even when you want to be hired full-time, you might only be recruited as a contract-based worker because contract-based workers have cost edition benefits and flexibility in the organization. Companies realize that employees don’t have to be in-office or on the payroll for certain functions. 

Post-COVID, with the perspective of cost-cutting companies, will be looking for people who are generalists, instead of specialists. And that is why young data scientists need to have more than domain-specific knowledge. They have to resell themselves to have an understanding of the overall field of data science. And the best way to do that is through a data science course in Bangalore that you can take from the comfort and safety of your home. 

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‘AI is the new electricity’ in a world that is reeling under myriad tech innovations. AI has the power to transform data collection, storage, and management, allowing the energy sector to catch up with today’s requirements while keeping the future needs in mind. Despite the size and enormity, the energy sector still heavily relies on manual work. This sector has a lot of catching up to do. And one way to look at this is to integrate data analytics into it. This development is also important to achieve the Sustainable Development Goal 7 (SDG7), aimed at ensuring access to affordable, reliable, sustainable, and modern energy for all by 2030.

Challenges and how tech can help

While there is no doubt that the demand for renewable energy is only going to increase, it is more important to understand the factors that hinder the scalability of it. There are several benefits of renewable energy today, but without intelligent forecasting and scheduling of the resources, industries cannot benefit to the fullest. Especially in a country like India, the unpredictability of the weather is a challenge. And it is where the use of intelligent tech interventions like data analytics and machine learning can help make data-driven decisions to predict weather conditions, maintain the supply chain, improve productivity, increase affordability while improving shortcomings. An amalgamation of these technologies will ultimately lead to the modernization of the energy sector, which is critical for every country’s economy. Efficient data management can change the face of the industry for good and fast track developments.

Big data and analytics can revolutionize the Renewable Energy sector by

– Data forecasting: Shortage of energy is a global problem. One of the primary requirements of the energy sector is predictive analytics. There is an urgent need to upgrade predictive analysis methods to cut costs, save energy, become adaptable to changing conditions, and improve end-user experience. It is where data analytics and big data can enhance the power of forecasting for better. The cost of error in the renewable energy industry is high, and forecasting can help avoid that and predict changes in demand, overloads, and possible failures as accurately as possible.

– Efficient resource management: Resource management is equally necessary for the energy sector. And with data analytics and predictive mechanisms, renewable energy suppliers will be able to make informed decisions. It helps them in preparing for demand well in advance, predicting problems at the grass-root level, dispatching their resources better and saving resources to the best possible extent. And these can translate to low energy consumption and bills for end clients.

– Intelligent storage of resources: Thanks to efficient resource management, there is a growing need to store renewable energy. In such a scenario, additional capacity and new management systems are of prime importance, and big data and analytics help efficient storage of renewable energy. They help by rightly optimizing energy storage.

– Improving safety and reliability: Data analytics and big data offer improved safety, efficiency, and reliability by helping companies understand usage patterns, identify energy leakage and the health of the devices.

– Predicting failure and prevents it: Energy can be very hazardous when handled poorly. And it is more than necessary to implement data intelligence to predict and prevent deadly disasters. For instance, AI can predict system overloads and warn of potential transformer breakdowns.

Advanced technologies like big data and analytics have had an enormous effect on every aspect of the modern world today, and the energy industry is no exception. However, the pace at which it is implemented must be fast-tracked, as it has the potential to revolutionize the sector. While AI is famously known as the technology of the century, it will be interesting to see how it makes its presence felt in the renewable energy sector, in the days to come. Implementation of data analytics and big data will be the most efficient decision stakeholders in the industry can make today to reap benefits in decades to come.

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ISLAMABAD:

Everything is data, even the reading of this article (online) through computer, tablet or smartphone.

By definition, data is a set of qualitative or quantitative variables – it can be structured or unstructured, machine readable or not, digital or analogue, personal or not. Traditional analysis tools and software can be used to analyse and “crunch” data. We all are creating heaps of data every day, through our online, and even offline, activities.

Big Data is a term used to describe a set of tools, methodologies and techniques to find value and insights into the raw and complex datasets. In the context of Pakistan, I will touch upon four aspects of Big Data – recognition, value, use and ownership.

In the case of recognition, while there are some private sector companies banking on collection, dissemination and analysis of the data, a visible absence of public sector focus is notable. Even the relevant authorities such as the Ministry of IT and Telecom are focused more on regulatory, that too personal data, aspects rather than recognising Big Data as an asset with huge potential.

In fact, the private sector players are mostly foreign companies rather than the national ones.

The issue of value follows lack of recognition, thus ignoring the potential. According to a report of MarketsandMarkets, the global Big Data market size will grow from $138.9 billion in 2020 to $229.4 billion by 2025 at a compound annual growth rate (CAGR) of 10.6%.

Bigger and more connected countries will generate more data, resulting in more value for the Big Data. The benefit of that value, however, depends on who gets hold of the Big Data and puts it to use.

Pakistan, being the fifth most populous country, with quite significant IT and connectivity infrastructure, is a goldmine of data.

The use of data, particularly Big Data, is a huge missed opportunity, but not too late though. Using Big Data and analytics is probably one of the best governance tools available at present, particularly for resource-constrained countries like Pakistan.

It doesn’t take much to analyse the Big Data, develop decision support systems, transparency tools and service delivery mechanisms. Although not exactly in the realm of data, there is importance of using blockchain technologies for ensuring transparency and traceability in governance systems, mechanisms and tools.

Ownership of data is probably the biggest challenge, not only for Pakistan. It is still a fluid area that needs development of good practices, norms and regulatory frameworks. There are, however, a few things that many countries are doing such as data domicile and data privacy requirements. The European Union’s General Data Protection Regulation is one such example.

Data protection

In Pakistan, there is a legislative initiative in progress titled Personal Data Protection Bill 2020. This, however, is focused on personal data protection and not encompassing enough to take care of troves of data that is gathered by various operators, including foreign companies, in Pakistan.

Moreover, the aforementioned draft bill focuses more on misuse of personal data but is silent on who and how much someone may use it, thus extracting value that may have been someone else’s.

The draft bill also has loose data retention and data domicile requirement, thus, giving carte blanche to data users.

Data is being generated every second, captured and put to various uses by one or the other. As consumers, we are willingly, yet unknowingly, giving data for free in the form of bits and bytes but once it is put together in the form of Big Data, these bits are converted into billions of dollars in direct and indirect value.

Who should take care of this? Regardless, someone out there is certainly doing it, if not the state of Pakistan.

Big Data is an asset and a capital. It has a value only if that value is discovered. Think of the world’s largest companies – often one would term these IT or tech companies, such as Google, Facebook, Amazon and Alibaba. These are mining on data, Big Data precisely.

Data keeps on adding value the more it is used. There is, however, a catch that you cannot catch the data once it goes out of hands. Every passing moment is adding to the quantity and value of data being generated in Pakistan but it is also taking the ownership and use further away.

The government of Pakistan should provide an enabling environment for the private sector to benefit from the Big Data, develop analytics and related tools to be used in businesses and governance, but most importantly develop a regulatory framework to deal with the data ownership issues. A reactive response in this area is not even an option.

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