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  Benefits of Big Data for Small Businesses Following are key benefits of big data for small businesses- 1. Quick Access to Information Big data makes the generated information available and accessible at all times for the businesses in real-time. Various tools have been designed for capturing user data and thus, businesses can accumulate the information in terms of customer behavior. This huge chunk of information is readily available for the businesses at their disposal and they can implement effective strategies for improving their prospects. 2. Tracking Outcomes of Decisions Businesses of any size can gain huge amounts of benefits from the data-driven analytics and this calls for the deployment of big data. Big data enable businesses to track the outcomes of their promotional strategies and giving the companies a clear understanding of what works well for them and improves their decisions to gain better results. Small businesses can tap on this information to know which of their brands are being perceived by their key customers. Based on this information, businesses can carry out accurate predictions regarding their techniques and at the same time minimize their risks. 3. Developing Better Products and Services Small businesses can use big data and analytics for determining the current requirements of their prospective customers. Big data can help in analyzing customer behavior based on their previous trends. A proper analysis of customer behavior and its associated data helps businesses to develop better products and services based on their past needs. Big data also determines the performance of certain products and services of the company and how they can be used to meet these demands. Big data now also allows the companies to test their product designs and determine flaws that may cause losses in case that product is marketed. Big data is also used for enhancing after-sales services like- maintenance, support, etc.  4. Cost-Effective Revenues   How Small Businesses Use Data Analytics •  One of the key applications of machine learning for small businesses is by using it for tracking their customers at various stages of the sales cycle. Small businesses have been using data analytics for determining exactly when a given segment of customers are ready to buy and when they’re going to do so. •  Data analytics are also used for improving customer services. Machine learning tools are now able to analyze the conversations taking place between the sales team and customers across various channels. These can provide greater insights into some of the commonly faced issues by the customers and these can be leveraged for ensuring that customers have a great experience with a product/service/brand. •  Data analytics have been providing the SMBs with detailed insights on operational aspects. Data analytics can be of great use when it comes to a detailed analysis of customer behavior. This, in turn, allows the business owners to learn the motivating factors for the consumers to buy products or services. This is of great value as the SMB owners can utilize this information for identifying the market channels to focus in the coming time and thus saving on the marketing spend and thus increasing the market revenue.   Data Analytics Trends in 2023 for Small Businesses 1. Emergence of Deep Learning We have been generating huge volumes of data every day and it is estimated that the humans generate 2.5 quintillions of data. Machines have become more adept and deep learning capabilities are continuing to rise in the coming time. Often considered as a subset of machine learning, deep learning uses an artificial neural network that learns from the huge volume of data. Its working is considered to be similar to that of the human brain. This level of functionality helps the machines to solve high and complex problems with great degrees of precision. Deep learning has been helping small businesses in enhancing their decision-making capabilities and elevating the operations to the next level. Using deep learning, the chatbots are now able to respond with much more intelligence to a number of questions and ultimately creating helpful interactions with the customers. 2.  Mainstreamed Machine Learning 3.  Dark Data Dark data is used for defining those information assets that the enterprises collect, process or store but have failed to utilize. It is that data that holds value but gets eventually lost in the middle. Some common examples of dark data include- unused customer data, email attachments that are opened but left undeleted. It is estimated that dark data is going to constitute 93% of all data in the near future and various organizations look to formulate steps to utilize it.

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How Small Businesses Can Mine Big Data

As buzzwords go, big data is currently one of the most powerful—and one of the most perplexing. It sounds like something only multinational conglomerates can afford. But the concept of analyzing very large amounts of data and looking within it for patterns, trends, and insights is one that nearly any business, large or small, can use to help make better decisions.

The big data you might not know you have

The key word in “big data” is big: The more data you have, the better it works. Want to gain insights into your finances? A few hundred sales records might tell you something, but a few million will turn up more trends and help you gain a deeper understanding of where your money is coming from. Want to get a bird’s-eye view of your customer base? Thousands of customers and prospects fed into a big-data tool will be more valuable than an analysis of, say, your top ten buyers.

How small companies are profiting from big data

The goal of any big-data effort is to improve your business. If you’ve traveled through a major airport lately, for instance, you’ve probably seen Vino Volo, a small chain of wine bars that can now be found in 28 airports. Vino Volo is using big data in the form of a mobile app developed by Punchh, which works as a loyalty program and referrals system. Punchh co-founder Sastry Penumarthy says Punchh “crunches lots of real-time data from mobile, social, and POS to automatically provide brands (in real-time) 360-degree insights about their customers and stores, including visits by location and time of day, orders including specific menu items, reviews and sentiments of reviews, and campaign response rates,” among other insights.

Riviera Partners uses big data for recruiting. The job placement company keeps a huge database of potential candidates that is constantly being updated. Searching this database for the right candidate involves not merely searching for keywords on resumes but by aggregating its own data on a candidate and cross-referencing it with public information (like LinkedIn profiles). Candidates are then scored based on all of these factors on a job-by-job basis before being further vetted and presented to the client.

How can your small business use big data?

If you’re a typical small business, just crunching along from day to day without any real strategic direction, the ability to finally get your arms around your business by digging into the data you already have probably sounds enticing. But big-data service providers don’t make it easy. There are literally hundreds of companies out there, all of which promise to open your eyes to your company’s future by “harnessing big data.”

These companies can be wildly dissimilar. For example, Tranzlogic provides a Web portal for merchants where they can track sales, how various locations are performing, and whether promotions are paying off. It uses “big data” analysis of your credit card transaction data to do this. Or consider MaxxCAT, which makes a network appliance and accompanying software to pluck data from your internal servers and hook those results into processing systems. It’s also a big-data service, but the two companies couldn’t be more different.

Knowing what kind of big data service to work with depends on the type of data you’re looking to analyze.

Big-data companies vary widely in scope and scale. This overview will help you understand the types of companies out there. Some of them are large-scale providers that can analyze data from a wide variety of sources. Others work in extremely narrow niches. Again, choosing a big-data partner depends entirely on the data. There’s no sense in signing with a provider that specializes in slicing and dicing Salesforce databases if you don’t use that system.

InsightSquared: This service is designed to analyze sales and the selling process, with a distinct focus on hooking into Salesforce and similar apps to examine your CRM database. You can further refine this by adding in data from QuickBooks, Zendesk, Google Analytics, and other sources. InsightSquared provides sales forecasts, a pipeline visualization, a marketing cycle report, and more. Pricing starts at $99 per month.

Canopy Labs: Canopy is designed to predict customer behavior and sales trends, offering a variety of scenarios for the future that you can use to help guide marketing and promotional efforts. (For example: Should you target loyal customers or try to bring back those who haven’t shopped with you for a while?) Supports Constant Contact, Salesforce, MailChimp, and more. Pricing ranges from free (up to 5,000 customers) to $250 per month (up to 100,000 customers).

Radius: A big-data tool primarily used to help identify sales targets and aid with lead generation, especially for businesses working with a large number of prospects. A big focus is correcting outdated customer information, so sales reps don’t go calling on shuttered businesses. The company says it aggregates data from more than 30,000 sources. Pricing is $99 per user per month.

Qualtrics: Big data comes to customer surveys, such as those “Tell us how we can do better” pop-ups you get at the end of a Web browsing session. Insights driven by Qualtrics can help with product and market research, ad testing, and even performance evaluations at the office. Pricing varies.

Qualtrics online survey platform helps with everything from product research to performance evaluation.

However, if your business decides to embrace big data, it doesn’t have to mean making a huge commitment to a service provider—contractually or financially. Identify one problem area—sales, finances, Web performance, etc.—and start mining your data for insights. In no time, you’ll be turning big data into big opportunity.

Enterprise Analytics: Managing And Making The Most Out Of Big Data

Why enterprise analytics is a business imperative and how it benefits businesses?

Leveraging data and excerpting insights from it has become indispensable for businesses. As the corporate world is increasingly dealing with the ever-growing information age, utilizing data can be a growth factor for them. This means they require the ability to manage and evaluate big data that can maximize the business value buried within their

Why Enterprise Analytics is Vital?

Leveraging data and excerpting insights from it has become indispensable for businesses. As the corporate world is increasingly dealing with the ever-growing information age, utilizing data can be a growth factor for them. This means they require the ability to manage and evaluate big data that can maximize the business value buried within their data sets . Integrating the right enterprise analytics strategy assists organizations as well as decision-makers to discover the tools and techniques they need to implement to process huge data sets and derive valuable insights from them in order to deliver better business decisions. The last couple of years have seen tremendous uptake in big data and how leading companies assessed and changed the analytics game. Many have propelled this trend with the introduction of data professionals or experts to their ranks, while some companies also deployed automation frameworks that have been able to create a singular data vision.Handling, processing and extracting meaningful information from the data businesses glean is a daunting task. This requires setting up a strategy for enterprise-level analytics tracking and reporting in addition to building a robust architecture with proper planning and coordination. Gathering any kind of data presents both value and risk to any enterprise. This is why a scalable and flexible enterprise analytics architecture is critical to the success of companies. An effective enterprise analytics strategy can create a comprehensive vision and end-to-end roadmap for managing and analyzing data. It can be beneficial in risk mitigation, mapping out companies’ data management architecture, identifying and eliminating redundant data, establishing responsibility and accountability, and improving data quality and more. According to MicroStrategy’s 2023 Global State of Enterprise Analytics report, around 65 percent of global enterprises have plans to boost their analytics spending in 2023. 79 percent of respondents in large enterprises reported they will invest more in 2023. Based on industry verticals, hospitality and government respondents are uncertain about their data-driven progress . 33 percent of respondents in hospitality and 31 percent in government reported that they feel their analytics programs are behind in comparison to the 17 percent overall average. Cumulatively, telecommunications, hospitality and retail industries lead all spending with 70 percent or more of enterprises in all three verticals. It is predicted that they will increase analytics and business intelligence spending in 2023. The report further reveals that only 16 percent of organizations’ analytics technology deployment is at the maturity level to include a sophisticated architecture for self-service analytics with governance, security frameworks, access to big data, and mobile and predictive technologies supported by a center of excellence for training and support. Moreover, Gartner foresees that by 2023, the majority of pre-built analytics reports will either be augmented or even replaced with automated insights. And by 2023, AI and deep learning techniques will be prevalent approaches for new applications of data science.

Top Ten Promising Big Data Companies To Look Out For In 2023

Big data companies are flourishing across the world with their data visualization features

Big data companies are in huge demand in the global tech market for their effective data management skills with data analytics and data visualization. Data-centric world is running towards different kinds of data to have a deep understanding of consumer behaviour in recent times. Big data helps to provide the utmost customer satisfaction and enhance customer engagement through these big data companies. Let’s explore some of the top ten big data companies in 2023 that are promising to leverage.  

1. Big Panda

BigPanda keeps businesses running with Event Correlation and Automation, powered by AIOps. With BigPanda, IT Ops, NOC, DevOps, and SRE teams prevent outages, lower operational costs and deliver extraordinary customer experiences. BigPanda is well known for reducing costs, improving performance and availability, and accelerating business velocity. BigPanda is ideal for Midsize Enterprises, C-suite, and IT Ops pros. BigPanda enables IT organizations to take costs out of their operations. By boosting efficiency, reducing escalations, slashing downtime, eliminating or shortening bridge calls, flattening headcount, reducing SLA penalties, and consolidating tools, BigPanda customers can reduce operating costs by up to 50%.  

2. BlueCloud Technologies

BlueCloud is a professional services company that serves enterprise customers in EMEA by providing a myriad of world-class services and solutions. BlueCloud provides various services like custom development, solution implementation, outsourcing, technical assessment, CRM solutions, cloud solutions, project management, and quality control. Collaboration, web portals and smart applications, infrastructure solutions, identity management, business process automation, and MiddleWare ESB are the solutions provided by the company.  

3. Clairvoyant 4. Cogito

Cogito’s speech analytics was deployed within population health and care management programs at premier health and insurance companies. Thousands of interactions were analyzed, generating millions of data points to further enhance the effectiveness of Cogito’s behavioural models. Implementing critical and transformative customer service improvement strategies has always been challenging. Cogito professional services are here to help its clients realize value from Cogito solutions by designing and delivering successful implementations and supporting ongoing client success.  

5. Cloudera

Cloudera helps innovative organizations across all industries tackle transformational use cases and exact real-time insights from an ever-increasing amount of data to drive value and competitive differentiation. Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Build, deploy and scale ML and AI applications through a repeatable industrialized approach and turn data into decisions at any scale, anywhere. Cloudera helps clients transform from both a technological and a practical standpoint, speeding up time to results with enterprise AI and ML. With the company’s modern, open platform and enterprise tools, Cloudera enables clients to build and deploy AI solutions at scale, efficiently, and securely, anywhere they want.  

6. Crunchbase

Crunchbase is a platform for finding business information about private and public companies. Crunchbase information includes investments and funding information, founding members and individuals in leadership positions, mergers and acquisitions, news and industry trends. Crunchbase’s best-in-class private company data offers insight into target companies’ teams, funding status, growth trends, tech stack, web traffic, investments, and more to personalize the outreach and increase engagement.  

7. CB Insights

CB Insights’ technology insights platform, intelligence analysts, and global network of executives and start-ups empower people to articulate compelling answers to difficult questions — about growth, the competition, and technology. The company aggregates and analyses massive amounts of data and use machine learning, algorithms, and data visualization to help corporations replace the three Gs (Google searches, gut instinct, and guys with MBAs) so they can answer massive strategic questions using probability, not punditry.  

8. Centerfield

Centerfield develops intelligent big data-driven marketing and sales technology utilizing real-time biddable media (RTB), automated call routing, and customized scripting. Our proprietary platform, Dugout, combined with our 1500-person sales and retention center delivers new customers at scale to many of the leading brands worldwide. Centerfield’s industry-leading platform automates end-to-end customer acquisition for millions of shopping experiences each year. The company accurately attributes every conversion to maximizing efficiency.  

9. CloudTrains Technologies

CloudTrains is a start-up for start-ups, SMEs, and enterprises. The company helps start-ups and businesses to work smart. CloudTrains is a mobile app and web development company with a world-class team of talented data scientists, app and web developers, designers, engineers, creative artists, and brand strategists. Headquartered at Gwalior and development center at Pune and Florida, USA. CloudTrains have more than 6+ years of experience in the IT Industry. The company is a one-stop destination for web and mobile app design and development services.  

10. Comsense Technologies

Predictive Analytics Vs Data Mining

Difference Between Predictive Analytics vs Data Mining

Hadoop, Data Science, Statistics & others

Head-to-Head Comparison Between Predictive Analytics and Data Mining

Below are the top 5 comparisons  between Predictive Analytics and Data Mining:

Key Differences of Predictive Analytics vs Data Mining

Below is the difference between Predictive Analytics and Data Mining

Process – The process of Data Mining can be summarised into six phases-

Business/Research Understanding Phase – Enunciate the project objectives and requirements in terms of the business or research unit as a whole

Data Understanding Phase – Collect and use exploratory data analysis to familiarize yourself with the data and discover initial insights.

Data Preparation Phase – Clean and apply a transformation to raw data so that it is ready for the modeling tools.

Modeling Phase – Select and apply appropriate modeling techniques and calibrate model settings to optimize results.

Evaluation Phase – Models must be evaluated for quality and effectiveness before we deploy. Also, determine whether the model achieves its objectives in phase 1.

Deployment Phase – Using models in production might be a simple deployment, like generating a report, or a complex one, like Implementing a parallel data mining process in another department.

Define Business Goal – What business goal will be achieved, and how does data fit? For example, the business goal is more effective offers to new customers, and the data needed is the segmentation of customers with specific attributes.

Collect Additional Data – Additional data needed might be user profile data from online systems or data from third-party tools to understand data better. This helps to find a reason behind the pattern. Sometimes Marketing surveys are conducted to collect data.

Draft Predictive Model – Model created with newly collected data and business knowledge. A model can be a simple business rule like “There is a greater chance to get convert the users from age a to b from India if we give an offer like this” or a complex mathematical model.

Business Value – Data Ming itself adds value to business-like.

Deeply understand customer segments across different dimensions.

Get performance patterns specific to KPIs (Eg. Is subscription increasing with active users count?)

Identify Fraudulent activity attempts and prevent them.

System performance patterns (Eg -Page loading time across different devices – any pattern?)

Vision – Helps to see what is invisible to others. Predictive analytics can go through a lot of past customer data, associate it with other pieces, and assemble them in the right order.

Decision – A well-made predictive analytics model provides analytical results free of emotion and bias. It provides consistent and unbiased insights to support decisions.

Precision – Helps to use automated tools to do the reporting job for you — saving time and resources, reducing human error, and improving precision.

The performance of predictive analytics is measured on business impact. For example – How well the targeted ad campaign work compared to a general campaign? No matter how well data mining finds patterns, business insight is a must to work predictive models well.

Future – The data Mining field is evolving very fast. Trying to find patterns in data with lesser data points with a minimum number of features with the help of more sophisticated models like Deep Neural Networks. Many pioneers in this field, like Google, are also trying to make the process simple and accessible to everyone. One example is Cloud AutoML from Google.

Predictive Analytics and Data Mining Comparison Table

Below are the lists of points that describe the comparisons between Predictive Analytics and Data Mining.

Basis of Comparison Data Mining Predictive Analytics

Definition Data mining is discovering useful patterns and trends in large data sets. Predictive analytics is extracting information from large datasets to predict and estimate future outcomes.

Importance Help to understand collected data better. E.g.:

A better understanding of customer segments.

Purchase patterns across geography or time.

Stock price timeline analysis.

GPS street data analysis.

Predict on top of data mining results by applying domain knowledge –

What customer will buy next?

What will be the customer churn rate?

How many new subscriptions will be started if this offer is given?

What is the amount of stock of a product  needed for the coming month?

Scope Apply Machine Learning algorithms like Regression, Classification to collect data to find hidden patterns. Apply business knowledge on data-mine patterns with any additional data needed to get business-valid predictions.

Outcome The output of data mining will be a pattern in data in the form of a timeline with varying distributions or clusters. But it won’t answer why this pattern occurred. Predictive analytics tries to find answers to the pattern by applying business knowledge and thus making it a more actionable piece of information.

People Involved Done mainly by statisticians and Machine Learning engineers who have a strong mathematical background to do feature engineering and creating ML models. Business-specific knowledge and a clear business objective are a must here. Business analysts and other domain experts can analyze and interpret the patterns discovered by the machines, making useful meaning out of the data patterns and deriving actionable insights.


As Rick Whiting said in InformationWeek, What’s next is what’s next. Predictive analytics is where business intelligence is going. Data Mining helps organizations in many ways, and one of the most important is creating a good foundation for Predictive Analytics.

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Data Analytics Infrastructure: Current Trends

Data analytics infrastructure is area that requires constant deep study to remain current.

The very term data analytics infrastructure is itself far from simple. It’s a wide ranging concept that comprises the many technologies and services that support the essential process of data mining for competitive insight. These many elements include managing, integrating, modeling and – perhaps most important – accessing the rapidly growing data sets that allow companies to better understand their business workflow and forecast market moves.

The challenge of data analytics is that it changes faster than you can say “business intelligence.” The technology itself is now undergoing rapid evolution, as is the techniques that practitioners are using. This is one sector where even an approach that has seen no refresh in a mere six months is already falling behind.

To provide a current snapshot, I’ll speak with Brian Wood, Research Director, Dresner Advisory Services. Wood will discuss the new report from Dresner, 2023 Analytical Data Infrastructure Market Study.

Among the questions we’ll discuss:

What use case for ADI platforms did most respondents list as a top priority? What does this mean for the ADI market?

It seem as if corporate standards have been a low priority for ADI, compared with security and performance. What changes do you think this trend will create?

Is cloud or on-prem more popular for ADI platforms? What about the hybrid platform?

Are there factors that make creating a coherent strategy for analytics projects difficult? (Like the range of innovation and the variety of ADI platforms.) How can business leaders deal with this challenge?

Your sense of the future of the ADI market, several years out?

Listen to the podcast:

Watch the video:

Edited highlights from the full discussion – all quotes from Brian Wood:

“One of the things that tends to help to limit this kind of [problematic] spread across the organization of different components is having a chief data officer, CDO or a chief analytics officer. Because that becomes a focus for them to make sure they have a cohesive and efficient analytic data infrastructure as opposed to a little of this here and a little of that there.

In most cases [the CDO] don’t really play the role of the cop trying to enforce it, although if you have the C in front of your title, you tend to get attention.”

“To me, the only difference between governance and compliance is where the requirements come from. Governance is placing requirements on yourself. They’re internally. Compliance is from external.”

“Corporate standards [for data analytics practices] aren’t important. If one person finds a tool that is purely cloud-based and web-based and it works well for them, they will go ahead and buy it.

A lot of these tools and products have freemium models where someone can put their personal credit card in and use it for a month and then of course once they get used to the tool they’re not gonna let it go, and it becomes part of your analytic data infrastructure.”

“One of the things that I find interesting is, even in the large organizations, they want everything in the Cloud, but they’re not starting from a green fields situation. They have lots of On-premise type of systems already.

But in order to get there from where you are today, you need a hybrid analytic infrastructure.

It has to report on the On-premise and the Cloud. But of course then you have multicloud as well. You have multiple public clouds, you have virtual private clouds. Having an infrastructure that will work with all of those, and I think particularly for the larger organizations that have been around for longer, it’s a stepping stone on the path if they wanna get to Cloud.

And most of them do. The survey says that is a preferred deployment approach for most industries and most functions. But in order to get there you have to go through the hybrid to get to a Cloud infrastructure.”

“So I’m often called an idealist, because I tend to look at the way things should be instead of the way they are. [chuckle]

So I’ll say, with a grain of salt, I will say that we will have AI capabilities that will enhance the way we do our jobs and not replace them. The future of work part aspect of it is one part of it, but realistically, we have models that do a lot of pieces of what a human brain does well, but there isn’t the master algorithm.

And so, what you’ll have is you’ll have the ability for an AI system to look at the different analytics in your organization and make recommendations, like, “This is good, but really you’re only using the trending. You’re not using the actual data.”

Obviously, now we’ve got models that beat the best chess players and all that. But you have to have something to model. So the way humans process information may or may not be the most efficient, but just taking that and putting it on a silicon substrate instead of soft wetware, so to speak – that helps a lot, but you still need the people to say, This is how I think. These are the connections that I make that led me to this conclusion.’”

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