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The demise of big data and the emergence of smart data are helping comply with demands

The era of 

Cremating big data The rise of smart data

The era of bigdata has come to an end. Data has been a vital component of creating strong, long-lasting, mutually beneficial relationships between businesses and customers for a long time. They provided a way for business executives to better understand the customers and fulfill their needs based on previous preferences. But very quickly, the night has changed. We have already bid farewell to bigdata in business , so what next? Yes, it is smart data . The emergence of smart data has leveraged a stronghold on companies to equip them to the improving demand. ‘ Bigdata ,’ the term that opens the door to over 800 million results in Google search has been ruling over us for the past two decades. Starting from offering a cost-efficient means of insight to optimizing return-on-investment and growth, bigdata in business has played a significant role. Unfortunately, all along the way, marketers had a hard time solving the riddles of data. Whether it is accumulating the right data, organizing it so it can be easily analyzed, or being able to extract useful insights, there are a number of challenges that data engineers are undertaking to extract insight. The technology sector is evolutionary. While some technologies garnered peak popularity at one instance of time, they gradually went on to evolve after being perceived that their data were over. The same thing is happening to bigdata . As the technology has reached a form of saturation, it is turning to be smart data . Data scientists have long been complaining about spending around 80% of their time cleansing, verifying, and preparing data. Fortunately, as smart data comes in handy with a well cleansed, verified and fertilized mechanism, the world finds it easy to handle. Bigdata is a great marketing term, but in reality, that’s all it is. The enormous amount of data is nothing useful without getting good insight from it. As companies become more familiar with data processing and service providers abstract away more complexity, bigdata in business will just become big. But data is not dead. It is just transforming to an easier form. By 2025, it is predicted that the global data sphere will be 175 zettabytes, up from 50 zettabytes in 2023. Henceforth, we are preparing for a future where the data explodes exponentially and we have sources to store and get the best out of it. Why is bigdata becoming useless? Let me explain this with an example. Recently, the volume of data is drastically growing. People also rely on data for many situations. According to a survey of Fortune 1000 executives by the Harvard Business Review, reliance on bigdata initiatives is on the rise. Unfortunately, there are some cases where data reliance has impacted badly on certain scenarios. In a data wrong gone incident, the portal was using more data than was called for. OfficeMax has sent a letter to an individual in Illinois, addressed to Mike Seay. The letter reported the death of his daughter in a car crash. But the worst case was that Seay’s daughter was killed in a car crash one year earlier. The scenario was even ruined as Seay was on his way to attend a counseling group of grieving parents when he received and read this letter. Smart data is digital information that is formatted so it can be acted upon at the collection point before being sent to a downstream data analytics platform for further data consolidation and analytics. The term smart data analytics is associated with the Internet of Things (IoT) and most of the data is extracted from smart sensor-embedded devices. To get the maximum out of smart data , one has to better understand the clues in the question around data. Besides from making data-driven decisions, smart data analytics pushes us to make creative initiatives. Data analytics were dependent on the famous Vs (Velocity, Variety, and Veracity) that big data carried. However, smart data also revolves around Veracity and Value. With smart data , we focus on valuable data and often smaller datasets that can be turned into actionable data and effective outcomes to address customer and business challenges. By putting in the context of purpose and context, smart data analytics makes the analysis and interpretation of data easy. For example, smart data is used in Open banking where UK-regulated banks have to give customers the option to allow access and control of their personal and financial data to TPPs.

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Big Data Definition & Analytics

The sheer volume of big data continues to grow as corporations and consumers depend on mobile devices, streaming, the Internet of Things (IoT), and other technologies that collect and use massive quantities of data. 

And with the growth of big data, the market is seeing a steady increase of tools and capabilities for storing and analyzing it. 

See below to learn about the top tools in the big data market and how enterprises are benefiting from the increased insights borne from big data.

“Big data” was first coined in the 1990s when technology experts began to recognize the quickly expanding pools of data in enterprises as well as the growing problem of processing and applying that data with existing technology.

Big data has grown across industries throughout the 21st century, and with that growth has come the development of different big data tools to handle the data.

Big data informs several business decisions and operations, but is especially helpful in the following categories:

Customer Analytics

Operational Analytics

Fraud Detection


Data Warehouse Optimization

Read next: The Pros and Cons of Edge Computing

The global big data market reached $208 billion in 2023 and is projected for a steady compound annual growth rate of 10%, reaching $450 billion by 2026, according to Expert Market Research. 

The growth is mostly attributed to a growing desire to make all business data actionable in a competitive marketplace, with the growth of IoT devices contributing to the expansion of big data solutions.

Although North America is leading the market in big data software adoption and general strategy, China is the fastest-growing country in this predicted time period, with Australia, India, and South Korea also increasing their big data investments at a healthy rate.

In order for software to be classified as appropriate for big data management, it must meet the criteria of the “3 Vs”: variety, volume, and velocity:


The tool is able to work with a variety of data types, whether structured, unstructured, quantitative, or qualitative.


Big data consists of terabytes and petabytes of data to manage. Big data software must have the capacity to store/analyze higher volumes of data than traditional data tools as a result.


Especially in a fast-developing IoT marketplace, big data solutions have to be able to work with data quickly and in real-time in order to produce desired results.

More on data variety: Structured vs. Unstructured Data

More on data variety: Qualitative vs. Quantitative Data

Understanding big data provides huge benefits to organizations that take the time to set up, uncover and analyze their data. 

Some of the top benefits that companies realize when they tap into their big data potential include:

A better understanding of customer behavior at various stages based on large amounts of data that might have previously gone unanalyzed. This information is particularly helpful in the entertainment, e-commerce, and tourism industries.

For organizations that want to simplify their network security and operations practices, big data makes it possible to develop AIOps and automate different network functions, such as application performance management and network monitoring. This frees up time for network administrators to spend on more strategic projects.

In industries, such as finance, banking, government, healthcare, and others with frequent and large transactions, big data analytics improves fraud detection, risk management, and general compliance.

Customers across industries use big data tools to make sense of their customer and product data at scale. 

They frequently rely on the big data analytics in these tools, but perhaps more importantly, they use these tools for the data visualizations and reports that make big data digestible for non-data professionals:

“We have been using [Hitachi Vantara’s] Pentaho Business Analytics for the past 3 years in our department due to its best services for data visualization and data analytics. It is open and easily added to any platform. It provides such an easy UI that non-technical persons can get the use and analysis results. Easily configurable and deployed at our organization.” –Software manager, manufacturing industry, software review at Gartner Peer Insights

Some of the top big data solutions in the market are:

Amazon Web Services:

AWS’s solutions for big data include cloud storage, databases, data warehousing, analytics, and machine learning services.

Hitachi Vantara:

This lineup features big data storage, DataOps, IoT services, and big data analytics.


The Salesforce-acquired tool offers big data analytics, business intelligence, and data visualizations


This big data platform offers a Hadoop distribution, plus data science and analytics tools.

Microsoft Azure

: The cloud platform offers storage, big data analytics, machine learning, data warehousing, and data lakes.


IBM’s big data solutions include cloud services, database management, data warehousing, analytics, and machine learning.


The Oracle suite of big data solutions includes cloud-based and on-premises database management, data integration, and analytics.


This offering primarily focuses on analytics for log and security data.


The solution features a set of big data integration tools.


The data science platform includes data mining, predictive analytics, and machine learning solutions

Finding the right DBMS solution: Oracle Database vs. Microsoft SQL Server

Learn more about what’s happening in big data here.

Top 8 Big Data Best Practices

Also see: Big Data Trends and Best Practices

Big Data can easily get out of control and become a monster that consumes you, instead of the other way around. Here are some Big Data best practices to avoid that mess.

Big Data has the potential to offer remarkable insight, or completely overwhelm you. The choice is yours, based on the decisions you make before one bit of data is ever collected. The chief problem is that Big Data is a technology solution, collected by technology professionals, but the best practices are business processes.

Thanks to an explosion of sources and input devices, more data than ever is being collected. IBM estimates that most U.S. companies have 100TB of data stored, and that the cost of bad data to the U.S. government and businesses is $3.1 trillion per year.

And yet businesses create data lakes or data warehouses and pump them full of data, most of which is unused or ever used. Your data lake can quickly become an information cesspool this way.

The most basic problem is a lot of the handling of this data is partially or totally off base. Data is either collected incorrectly or the means for collecting is not properly defined. It can be anything from improperly defined fields to confusing metric with imperial. Business, clearly, grapple with Big Data.

That’s less of a problem with regular, routine, small levels of data that is used in business databases. To really foul things up you need Big Data, with petabytes of information. Because the data scales, so does the potential for gain or for confusion. So getting it right becomes even more important.

So what does it mean to ‘get it right’ in Big Data?

Big Data Best Practices: 8 Key Principles

The truth is, the concept of ‘Big Data best practices’ is evolving as the field of data analytics itself is rapidly evolving. Still, businesses need to compete with the best strategies possible. So we’ve distilled some best practices down in the hopes you can avoid getting overwhelmed with petabytes of worthless data and end up drowning in your data lake.

1) Define the Big Data business goals.

IT has a bad habit of being distracted by the shiny new thing, like a Hadoop cluster. Begin your Big Data journey by clearly stating the business goal first. Start by gathering, analyzing and understanding the business requirements. Your project has to have a business goal, not a technology goal.

Understanding the business requirements and goals should be the first and the most important step that you take before you even begin the process of leveraging Big Data analytics. The business users have to make clear their desired outcome and results, otherwise you have no target for which to aim.

This is where management has to take the lead and tech has to follow. If management does not make business goals clear, then you will not gather and create data correctly. Too many organizations collect everything they can and go through later to weed out what they don’t need. This creates a lot of unnecessary work if you just make abundantly clear up front what you do need and don’t collect anything else.

2) Assess and strategize with partners.

A Big Data project should not be done in isolation by the IT department. It must involve the data owner, which would be a line of business or department, and possibly an outsider, either a vendor providing Big Data technology to the effort or a consultancy, to bring an outside set of eyes to the organization and evaluate your current situation.

Along the way and throughout the process there should be continuous checking to make sure you are collecting the data you need and it will give you the insights you want, just as a chef checks his or her work throughout the cooking process. Don’t just collect everything and then check after you are done, because if the data is wrong, that means going all the way back to the beginning and starting the process over when you didn’t need to.

By working with those who will benefit from the insights gained from the project, you ensure their involvement along the way, which in turn ensures a successful outcome.

3) Determine what you have and what you need in Big Data.

Lots of data does not equate good data. You might have the right data mixed in there somewhere but it will fall to you to determine it. The more haphazardly data is collected, the more often it is disorganized and in varying formats.

As important as determining what you have is determining what you don’t have. Once you have collected the data needed for a project, identify what might be missing. Make sure you have everything before you start.

The bottom line is sometimes you have to test the data it and review the results. You might be surprised to find you are not getting the answers you need. Best to find out before you plunge head first into the project.

4) Keep continuous communication and assessment going.

Effective collaboration requires on-going communications between the stakeholders and IT. Goals can change mid-way through a project, and if that happens, the necessary changes must be communicated to IT. You might need to stop gathering one form of data and start gathering another. You don’t want that to continue any longer than it has to.

Draw a clear map that breaks down expected or desired outcomes at certain points. If it’s a 12-month project, check in every three months. This gives you a chance to review and change course if necessary.

5) Start slow, react fast in leveraging Big Data.

You first Big Data project should not be overly ambitious. Start with a proof of concept or pilot project that’s relatively small and easy to manage. There is a learning curve here and you don’t want to bite off more than you can chew.

Choose an area where you want to improve your business processes, but it won’t have too great of an impact in case things go wrong or badly. Also, do not force a Big Data solution approach if the problem does not need it.

You should also use Agile techniques and the iterative approach to implementation. Agile is a means of operation and it is not limited to development. What is Agile development, after all? You write a small piece of code, test it eight ways from Sunday, then add another piece, test thoroughly, rinse, repeat. This is a methodology that can be applied to any process, not just programming.

Use Agile and iterative implementation techniques that deliver quick solutions in short steps based on current needs instead of the all-at-once waterfall approach.

6) Evaluate Big Data technology requirements.

The overwhelming majority of data is unstructured, as high as 90% according to IDC. But you still need to look at where data is coming from to determine the best data store. You have the option of SQL or NoSQL and a variety of variations of the two databases.

Do you need real-time insight or are you doing after-the-fact evaluations? You might need Apache Spark for real-time processing, or maybe you can get by with Hadoop, which is a batch process. There are also geographic databases, for data split over multiple locations, which may be a requirement for a company with multiple locations and data centers.

Also, look at the specific analytics features of each database and see if they apply to you. IBM acquired Netezza, a specialist in high-performance analytics appliances, while Teradata and Greenplum have embedded SAS accelerators, Oracle has its own special implementation of the R language used in analytics for its Exadata systems and PostgreSQL has special programming syntax for analytics. So see how each can benefit your needs.

See also: Big Data virtualization.

7) Align with Big Data in the cloud.

The first is using it to rapidly prototype your environment. Using a data subset and the many tools offered by cloud providers like Amazon and Microsoft, you can set up a development and test environment in hours and use it for the testing platform. Then when you have worked out a solid operating model, move it back on premises for the work.

8) Manage your Big Data experts, as you keep an eye on compliance and access issues. 

Big Data is a new, emerging field and not one that lends itself to being self-taught like Python or Java programming. A McKinsey Global Institute study estimates that there will be a shortage of 140,000 to 190,000 people with the necessary expertise this year, and a shortage of another 1.5 million managers and analysts with the skills to make decisions based on the results of analytics.

First thing that must be made clear is who should have access to the data, and how much access should different individuals have. Data privacy is a major issue these days, especially with Europe about to adopt the very burdensome General Data Protection Regulation (GDPR) that will place heavy restrictions on data use.

Make sure to clear all data privacy issues and who has access to that sensitive data. What other governance issues should you be concerned with, such as turnover? Determine what data, if any, can go into the public cloud and what data must remain on-premises, and again, who controls what.

How Big Data Analytics Is Transforming The Casino Industry

As one of the world’s most cutthroat markets, it is no surprise that the casino business has jumped on the big data analytics bandwagon. Casinos may improve their tracking of consumer behaviour, learn more about their patrons’ requirements and preferences, and streamline their operations using big data analytics. Notably, some of the casinos reviewed on SF Gate are utilizing big data analytics to tailor their offering to suit specific customers. The article takes an in-depth look into how casinos utilize big data analytics to streamline their operations. 

Identifying customer preferences and trends

With big data analytics, casinos may better understand their customers’ tastes and behavior by gathering, analyzing, and drawing conclusions from massive volumes of data. Customer profiles, purchase histories, gaming tendencies, loyalty program participation, and other types of information may be collected. Casinos may use this data to understand their patrons better and cater to their needs. For instance, they may be able to tell which games are the most well-liked among people of a certain age or gender. In addition, they could ascertain which campaigns are most successful in generating client engagement or loyalty. Now that they have this information, they may make educated guesses about improving their business for optimum profit.

Promoting responsible gambling practices

There has been a rise in the importance of big data as a tool for responsible gaming. Operators may learn a lot about their customers’ habits by gathering and analyzing a lot of data. The data may help identify problem gamblers before they fall too far into debt. One other way operators may benefit from big data is by gaining a deeper understanding of client preferences and using that knowledge to improve the quality of their offerings. The operators may provide more customized incentives and promotions for each player by, for instance, monitoring their spending habits.

Ensuring customer privacy using big data analytics

There are several measures casinos use to protect their client’s personal information. Encryption is one of the most vital methods. Casinos utilize sophisticated encryption methods to protect all player information, making it almost difficult for unauthorized parties to decrypt player records. To further ensure the privacy of their patrons, casinos often use anonymization methods. Before using data for analytics, it must be cleansed of any identifying characteristics. To round things out, casinos have stringent procedures limiting who may access consumer data and how it can be utilized. To guarantee that the most up-to-date measures always protect the privacy of their customers, casinos examine and revise their policies regularly.

Automated decision making using big data analytics

Implementing fully automated decision-making might drastically alter the future of online gambling. Casinos can now make better, more efficient judgments regarding gameplay thanks to the use of algorithms and AI. The casino’s bottom line and the patrons’ overall satisfaction both stand to benefit from this. By identifying questionable behavior and taking action, automated decision-making might also assist in eliminating fraud and cheating in online casinos. Further, player data might be analyzed using automated decision-making to learn about preferences and design games appropriately. Lastly, automated decision-making might be used in developing new games and enhancing current ones to maintain player interest and satisfaction. As a result of all these positive implications, it’s evident that automated decision-making merits additional exploration for its possible uses in the online gambling sector.

Challenges arising from implementing big data analytics

The massive volume of data is a significant obstacle for casinos attempting to use big data analytics. Customers’ gaming habits, financial transactions, and preferences contribute to the massive amounts of data generated by casinos. Gaining valuable insights into client behavior requires collecting, analyzing, and storing this information which is challenging. Casinos also must safeguard this information against hacking attempts. Validating the accuracy and consistency of the analytics is another obstacle to overcome. Last but not least, casinos should think about how they will put the results of big data analytics to use to boost their business. 

Predictive analysis using big data analytics

Lastly, casinos may use predictive analytics to jump on the competition and maintain long-term profitability by predicting industry trends. Using predictive analytics, casinos can see how varied incentives affect patrons’ propensity to return and spend. It enables businesses to optimize their offerings for maximum profit with little sacrifice to client satisfaction.

How Big Data Is Important For Compliance

Also, the rise in data insecurity has led to increased regulations on big data to guarantee its safety. If your organization is handling big data, you should develop a mechanism to ensure that you analyze it appropriately and seal all the loopholes that may lead to breaches.

Definition Of Big Data?

At this point, you are probably wondering whether your organization is dealing with big data or not. To resolve your quagmire, we’ll breakdown the features that are used to classify data as big. They include:

•   Volume.

Big data is considered to be voluminous.

•   Velocity.

The rate at which your organization receives data is crucial in determining its nature. If the reception is fast, then you’re likely dealing with big data.

•   Variety.

How many types of data are you dealing with? Well, if you have data from different sources and in different forms, then you are dealing with big data. For example, you could be having structured, semi-structured, and unstructured data in your system


The data stored in your organization may include data that you have obtained from your customer or that which you collected from public sources. To fulfill the requirements of various compliance bodies, you will be required to provide a thorough report indicating the type of data that you receive, how you utilize it, whether you share it with vendors, and the security measures in place to prevent data breaches.

However, analyzing big data is not easy! You will require highly

sophisticated analytical tools

to obtain accurate and reliable results. As such, you need to involve experts to guarantee a quick and easy compliance process. This process may be overwhelming, but the overall benefits will be worth the hassle.

The Link Between Big Data And Compliance

Big data directly affects your compliance process since you’ll be required to account for its flow in your organization. The regulatory bodies will be keen to evaluate every data handling stage, including collection, processing, and storage. The primary reason for the extensive evaluation is to ensure that your data is safe from any cyberattacks.

To obtain compliance status, you should develop security strategies that will protect your data. During the analysis, you’re expected to show how each of the risk mitigation strategies works and its efficiency level. This detailed report on your data security systems will make it easier for your organization to be certified.

Unlike institutions that deal with small data, you will require to use sophisticated analysis tools. Also, you must employ qualified professionals to analyze the data, identify security threats, and recommend mitigation strategies. As such, handling big data during the compliance process will require more resources compared to when handling small data.

However, you will harness the benefits of handling big data, which helps in getting clear predictive reports on the likelihood of an attack. When using this

form of data during auditing

, the auditors are likely to develop more stringent measures than if they used small data. As such, the use of big data analytics is one of the surest ways to develop some of the most reliable security systems in your organization.

How Big Data Is Applied In Compliance Process

One of the prerequisites for any compliance process is proof of detailed risk management strategies. Big data aids in developing a compressive risk evaluation system by:

•   Prevention of Fraudulent Activities.

The use of big data boosts the predictive analysis approach, which is an ideal way to detect criminal activities such as money-laundering. When a compliance officer uses big data for internal audits, they detect cyber threats and act on averting their occurrence. This accelerates the compliance process and builds confidence among your clients.

•   Managing Third Parties Risk.

It’s crucial that you adequately manage the risk associated with sharing your data with vendors if you’re in getting compliance certifications. Big data analytics will help you to manage the risks associated with the vendors. You’ll achieve this through a thorough analysis of their ability to protect your data before sharing with them.

•   Helps in Managing your Customers.

Before you get any compliance certification, you are required to prove that your customers are satisfied with how you handle their data. If you apply big data analytics, you will understand the behavior of your customers, which will directly influence your decision-making process, thus making the compliance process easier


How Companies Are Wrestling Over Big Data

Big data today is considered as one of the most prevailing, trending and emerging technology. As the escalation in data is rapidly increasing, industries are progressively adopting this technology to obtain better insights from the data. Even businesses are onboarding big data analysts to assist their organizations to fulfill customers’ requirements and demands. And analysts do this process by curating valuable insights from raw data. There is a wide array of companies worldwide who driving big data technology and services market, including IBM, Oracle Corporation, Microsoft Corporation, HP, SAP SE, Amazon Web services, Dell Incorporation (EMC), Teradata, Cisco Corporation, SAS Institute. Moreover, companies like Salesforce CRM can see the mushrooming demand for big data and the analytics involved. They are planting their stake deep in the foundation of the industry no matter the cost.  

Wrestle Over Big Data

Recently, Salesforce announced to purchase all-stock of Seattle-based big data firm Tableau Software Inc for $15.3 billion, and marking the major acquisition for CRM since its inception. As part of the acquisition, Salesforce is paying a roughly 45% premium from DATA’s closing price on Friday. This is a surprising premium to pay for a firm that wasn’t low-priced, to begin with. And this deal is expected to be finalized 1st October of this year. This announcement is following Google’s GOOGL declaration that acquired Looker for $2.6 billion, with 62.5% premium over the prior valuation, which came into the view last week. As a business intelligence and data analytics platform, Looker has been competing on the heels of Tableau. The fight over cloud computing and big data is starting to escalate and is expected to more consolidation as these firms affray for market share. Google and Salesforce are both shaping their terrain before anyone else can dive in to steal it. Though, the premiums that these companies have to pay to cover the market of big data may not be worth the price at this late stage in the business cycle. With the acquisition of Tableau, the anticipation is that Salesforce will be able to assimilate business analytics across its different offerings, where data for different industries and products exist. Customers, with more analytics software, could pull data from the various Salesforce clouds they offer, including customer relationship management (CRM), marketing and sales, among others. Salesforce’s deal comes days after Google acquired big data analytics company Looker for $2.6 billion and exceeds the $5.9 billion that the cloud-based software company paid to take U.S. software maker MuleSoft last year.  Mulesoft assists with application and data integration so that big data can work together impeccably.  

Customer Relationship Management with DATA

CRM and DATA, these two segments have some obvious interactions as Salesforce being the biggest CRM firm and Tableau as a leading data analytics company. With blending these efforts will improve Tableau’s ability and offer to scale, and further expand and entrench Salesforce’s reign in the cloud and big data marketplace. Though for investors, Salesforce is going to have to prove the value of this cumbersome acquisition, outside the further revenue growth it will bring. Also, pricing pressure from Microsoft and Google could mean a tough battle ahead as well.

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