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Difference Between Predictive Analytics vs Descriptive Analytics

Predictive Analytics:

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Descriptive Analytics:

Descriptive Analytics will help an organization to know what has happened in the past; it will give you past analytics using stored data. For a company, it is necessary to know the past events that help them to make decisions based on the statistics using historical data. For example, you might want to know how much money you lost due to fraud.

Head to Head Comparison Between Predictive Analytics and Descriptive Analytics (Infographics)

Key Differences Between Predictive Analytics and Descriptive Analytics

Below is a detailed explanation of Predictive Analytics and Descriptive Analytics:

Descriptive Analytics will give you a vision of the past and tells you: what has happened? Whereas Predictive Analytics will recognize the future and tells you: What might happen in the future?

Descriptive Analytics uses Data Aggregation and Data Mining techniques to give you knowledge about the past, but Predictive Analytics uses Statistical analysis and Forecast techniques to know the future.

Descriptive Analytics is used when you need to analyze and explain different aspects of your organization, whereas Predictive Analytics is used when you need to know anything about the future and fill in the information that you do not know.

A descriptive model will exploit the past data that are stored in databases and provide you with an accurate report. A Predictive model, identifies patterns found in past and transactional data to find risks and future outcomes.

Descriptive analytics will help an organization to know where they stand in the market and present facts and figures. Whereas predictive analytics will help an organization to know how they will stand in the market in the future and forecasts the facts and figures about the company.

Reports generated by Descriptive analysis are accurate, but the reports generated by Predictive analysis are not 100% accurate it may or may not happen in the future.

Predictive Analytics and Descriptive Analytics Comparison Table

A king hired a data scientist to find animals in the forest for hunting. The data scientist has access to data warehouse, which has information about the forest, its habitat, and what is happening in the forest. On day one, the data scientist offered the king a report showing where he found the highest number of animals in the forest in the past year. This report helped the king to make a decision on where he could find more animals for hunting. This is an example of Descriptive Analysis.

Basis of Comparison Descriptive Analytics Predictive Analytics

Describes What happened in the past? By using the stored data. What might happen in the future? By using the past data and analyzing it.

Process Involved Involves Data Aggregation and Data Mining. Involves Statistics and forecast techniques.

Definition The process of finding useful and important information by analyzing huge amounts of data. This process involves forecasting the future of the company, which is very useful.

Data Volume It involves processing huge data that are stored in data warehouses. Limited to past data.

Examples Sales report, revenue of a company, performance analysis, etc. Sentimental analysis, credit score analysis, forecast reports for a company, etc.

Accuracy It provides accurate data in the reports using past data. Results are not accurate, they will not tell you exactly what will happen, but they will tell you what might happen in the future.

Approach It allows the reactive approach While this is a proactive approach


In this blog, I have specified only a few characteristics of the difference between Predictive Analytics and Descriptive Analytics; the result shows that there is an important and substantial difference between these two Analytical processes.

There is an increase in the demand for analytics in the market. Every organization is talking about Big Data these days, but it is just a starting point for creating valuable and actionable insights on the organization’s data. Therefore, analytical processes like Predictive Analytics and Descriptive Analytics will help an organization to identify how the company is performing, where it stands in the market, any flaws, any issues that need to be taken care and many more. By applying these analytical processes in business, you will know both the Insight and the Foresight of your business.

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Predictive Analytics Vs Data Mining

Difference Between Predictive Analytics vs Data Mining

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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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The Roi Of Predictive Analytics Platforms For E

Predictive Analytics are your ticket to higher revenues

In case you haven’t spent much time with web analytics, I’ll start by giving you this definition of predictive analytics (PA), courtesy of Wikipedia:

‘Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.’

Whereas with descriptive statistics you learn what data can and can’t describe your customers’ behavior, with predictive analytics you learn what to do next — that is, use data about past actions to make predictions about future actions.

Obviously, if you can discover and anticipate what your customers — and earlier-sales-stage prospects — really want from your brand, you’ll hold the ‘golden ticket’ to higher revenues and customer loyalty. As a result, you’ll become a rockstar analyst on your team.

This graphic below shows how three well-known brands use PA to serve up more customer-centered experiences.

In what follows I’ll first share the quantitative benefits of using PA platforms.I’ll then discuss the qualitative (and highly strategic) benefits of integrating these apps.

Quantitative benefits of PA platforms

Most e-commerce companies focus a lot on the Revenue Per Visitor (RPV) and Monthly Recurring Revenue (MRR) metrics. But boosting recurring monthly profits is even better. PA apps can help you do this by 1. driving up Average Order Value (AOV) and 2. offering optimal pricing for each visitor.

Higher order values from up-sells

First, let me define what I mean by ‘up-sell.’ An up-sell happens when a brand offers one or more complementary products (or accessories) together with the base product. If, after you’ve just decided to buy a new bike, the sales clerk says, ‘Here are the helmet and gloves I recommend to go with it,’ he’s doing an up-sell (actually two of them).

Up-sells are the ‘low hanging revenue fruit’ of e-commerce selling. Why? Because your prospect needs them to get a complete solution (for the previous example, to bike around the neighborhood safely). So, the better you can offer the right accessories at the best time and place, the more likely it is that a given visitor will add one or more of them to her order.

That’s where PA platforms come in. Based on the interaction and transactional data it collects over time, a PA algorithm can first determine in which segment a given visitor fits. During the user’s first visit, the algorithm can then show some ‘best fit’ accessories for the base product being shopped.

Higher profit margins with optimal pricing

In case you didn’t know, there’s a lot of price finessing going on in the world. For example, the person sitting next to you on your previous flight probably didn’t pay the same airfare as you did (perhaps even a lot less). Airlines were forerunners in the ‘dynamic pricing’ realm, and e-commerce PA platforms have now taken this technique to the next level.

For example, if you’ve visited an e-commerce site three times, signed up for its ‘special offer’ emails, viewed a particular product page three times, and hovered over the ‘add to cart’ button twice, you obviously have a higher engagement level than a first-time visitor who hasn’t yet hit a product page.

In such cases, a smart PA platform could push a message saying, ‘I see you’ve viewed [product X] a few times. How does a 15% discount sound?’, after which you could offer three choices:

‘Sounds good – add to cart and apply discount’

‘I have a question – let’s chat’

‘No thanks’

Some PA platforms will even adjust the pricing for you automatically. Let’s say, for example, that your visitor lives in a high-income Zip Code. You could offer this visitor the product with a default price of $XX. But the algorithm could offer a visitor from a middle-class Zip Code their the product at 0.85 * $XX. (Assumption: In both cases, the company would earn a good profit margin.)

Qualitative (and strategic) benefits of PA platforms

On the qualitative side, the benefits might not boost your tactical KPIs, but they can certainly inform your future marketing tactics and boost your team’s operational efficiency.

Gain business intelligence from usage reports

Since all of the leading PA platforms include reporting capability, you can easily run reports that show things like:

RPV based on user group.

RPV based on key visitor interactions (e.g. launched one or more chat sessions).

RPV based on correlations with other variables (how far down the Product page the visitor scrolled, and the pages viewed beforehand).

For example, let’s say you discover that (transacting) customers who view videos have a 20% higher RPV. You should then direct your designers to make the ‘play video’ interaction more enticing (for example by making the ‘play’ icon more visible, or the splash image more interesting).

Knowing these things — which amount to business intelligence — you can then hypothesize as to why these correlations are happening, and dream up new A/B tests ideas that capitalize on these findings.

Build a data-driven marketing strategy with the RACE Framework

As we know, working in a golden age of data-driven marketing, applying data and critical thinking to inform our marketing strategies can turbocharge marketing performance. However, misinformed strategies or decisions made based on misleading analysis can spell disaster later down the line.

Our RACE Framework is a simple strategic marketing structure designed to help marketers plan, manage, and optimize their marketing strategies using key insights from their customers’ journeys.

Book your free 1-2-1 consultation call with a member of the team today and take the chance to revisit your marketing strategy, and discuss your opportunities within the context of the RACE Framework. Get started today

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PA platforms increase your team’s efficiency

Without a ‘core’ PA platform, your marketing optimization team requires at least these people: an optimization analyst, a user experience designer, a software developer and a product owner (to negotiate with the business which tests to run). So you can best make sense of the reams of visitor and customer data flowing in daily, a data scientist would be great to have as well. These people would need to be proficient with a growing set of tools.

It’s a lot to manage. Besides that, the stakes are high. A failed split test or two could set your team back, both in terms of schedule and political clout.

That’s why these PA platforms are so valuable. By integrating your current analytics, predictive analytics, marketing campaigns, split tests and reporting into a single suite, they reduce the size of your team and keep everyone on the same page. Sure, PA apps do require a couple of weeks of learning ramp-up time, but once you’ve done that your team can focus on your core tasks of designing campaigns, running optimizations, and collecting insights.

Two case studies

I get it; what your CFO cares most about is financial results — in the form of revenue growth, improved operational efficiencies, and return on investment (ROI). So I’ll share some results, achieved through the use of two leading PA platforms.

Case Study: UGG Boots product page optimization

Here’s an example case study from HiConversion, a PA platform that’s been around for over 10 years.

For the parent company of UGG Australia, a seller of fashionable boots and footwear, HiConversion’s platform ran several variations of the ‘Heirloom Lace-up Boot’ product page. Their algorithm discovered a new version of the page that produced a 14.73% revenue lift within a six-week timeframe. In addition, the study found that the single biggest contributor to conversion lift came from a very small change in the look and feel of the checkout button. (In the chart below, ‘C-buttons’ refers to the checkout button colour.)

Something that’s important to reiterate: this revenue gain was for a single product page. UGG could run similar tests on other product pages, or pages leading up to them, over time, to produce additional revenue lifts. They could apply these learnings to other buttons on their visitors’ paths to this conversion.

Case Study: HelloFresh customer experience optimization

Here’s a case study for HelloFresh, a provider of ‘easy-to-cook meals in a box’ for a monthly subscription fee.

Partnering with HelloFresh, PA platform DynamicYield launched numerous campaigns aimed at increasing customer loyalty by serving up more personalized experiences at multiple customer touchpoints. Their stated goals were to:

Reduce the tendency of customers to cancel their subscriptions due to a lack of awareness of ‘hold my deliveries’ option.

Educate customers to plan their online-delivered meals weeks ahead of time.

The result of their optimizations: double-digit increases in both conversion rate and RPV. These results were achieved within the constraints of their existing technical ecosystem.

PA platforms could be your star player

Predictive analytics algorithms and the e-commerce optimization platforms they power have proven they can produce significant month-over-month and annualized revenue lifts for many top brands. Equally important, the reporting tools built into these platforms provide the insights e-commerce companies need to inform both UX optimizations and the rest of your digital marketing strategy.

Even if you have a talented team, they simply can’t make sense of the reams of data your e-commerce website and related pages collect every day. On the other hand, smart, data-rich PA platforms, if seeded with the right customer data and business rules, can become a star player on your team. And one that won’t demand a big raise next year.

Business Analytics Vs Business Intelligence

Business Analytics vs Business Intelligence

Business Intelligence is one of the most important aspects of data analysis and is an integral part of modern companies. The definition of business analytics techniques is somewhat ambiguous and is constantly changing according to the changing dynamics of the companies. In a nutshell, business analytics can be defined as a set of applications, practices, skills, and technologies that help companies make strategic and vital decisions, thereby allowing the company to achieve its goals and ambitions.

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After understanding the importance and immense potential of data analytics, many brands and organizations have started investing many resources in them. However, most of this data analytics is limited to dashboards and reports, whereas the field of data analytics is large and has many more possible opportunities. While the popular forms of data analytics are essential, it is necessary to understand that many forms of data analytics can come together to help brands become empowered in their decisions and choices. At the same time, it is essential to remember that companies are becoming more independent and keen on expanding their horizons through technology, so they must identify the value of data and its interaction at all possible stages.

The ability to break down concepts and gain a proper insight into how data functions can help companies build and manage applications independently. At the same time, this insight can help companies gain knowledge about how various units of a company work together on the one hand and the requirements of the IT sector to develop products and services that can enable effective communication and goal achievement hand.

The article on Business Analytics vs Business Intelligence search is structured below.

Business Analytics vs Business Intelligence Infographics

Below infographics on Business Analytics vs Business Intelligence throws light on the significant differences between the two.

Why is understanding data so important for companies?

While it is easy to understand why data is an essential aspect of modern companies and brands, there are also also certain pitfalls related to it. The first and most important is security, while the other two include integrity and accuracy, which are equally, if not more important. Once these three things have been guaranteed, determining effective results through data analysis is the only important thing left. Every company knows that data is used to provide valuable insights. When brands are armed with these insights, they can make decisions that improve their overall functioning and management. However, rarely is data used in a raw state; they have to be processed and presented so that they can apply strategically and comprehensively.

The latest analytical tools make it much easier for companies to gain these insights, but there is always a journey to make this data usable and valuable. Maintaining data accuracy at all stages is extremely important because inaccuracy in data can lead to wrong insights, and,, if implemented,, can affect the entire functioning of the company negatively. That is why the quality of the data sample is much more important than the quantity of data. Many companies,, instead of focusing on the quality, focus on gathering large amounts of data without thinking about whether it is correct or incorrect. Added to this, integrity plays a vital role in data analytics.

What is business intelligence skills?

We can analyze and investigate business performance using multiple methods to restructure it and achieve profitable gains and solutions. Top analytics consulting firms believe that business intelligence skills and analytics techniques will undergo significant changes and greater adoption in the coming years. Many analytics feel that companies will now shift from information technology reports to developing business intelligence tools capable of delivering informed choices about companies’ growth strategy and development. These will lead to four significant changes, faster processing capabilities, mobile applications, social decision-making models, and more spending on solutions providers.

Business analytics techniques have the power to process data at a much more rapid pace.

The amount of data available in a company is almost endless. To make sense of this data, there is a need to handle this vast data systematically and quickly. Today, BI analytics has gone mainstream, and even small and new companies are looking at using this technique to harness the immense potential in the market. Besides, many companies need this technology to forge ahead and explore newer opportunities and challenges. That is why analytic marketers are searching for new methods to create business analytical tools that can quickly process data and can be adopted by companies across different sectors. With tools that can be used across IT teams, these business analytical tools redefine how companies function and carry.

Business analytical tools are at the next stage of development, namely mobile applications.

Mobile smartphones are gaining rapid acceptance across the globe. According to a new report, almost 2 million worldwide will have smartphone access by the end of this year. Business marketers must look for new ways to integrate smartphones into business analytics techniques. Besides, many marketers and company professionals rely on mobile phones to keep them updated on the functioning of their company, especially when they are traveling or away from their offices. Business analytics companies are looking to invest in mobile BI functions, and software designers will soon look at manufacturing products aimed at mobile phones rather than desktop users. With many companies and brands already going mobile, business analytics techniques on smartphones already have a ready audience.

Business analytical tools should enable companies to make decisions on social platforms.

Social media platforms are viral and present in almost all countries worldwide. Today, all companies are on social media platforms, making it essential to have business analytical tools that integrate social network capabilities with decision-making capabilities. While this might be a little tricky and complex, integrating business analytical skills with social networking may no longer be an option but a requirement in the coming years.

Increased spending on business analytics techniques consulting

With so many complex and practical applications available in the market, experts feel that there will undoubtedly be an increase in business analytics techniques consulting, especially in the coming years. Companies are pressuring business analytics firms to provide faster and better tools to help achieve their business analytical goals.

Business Analytics vs Business Intelligence, How are they different?

This is how business analytics techniques can help companies. Now coming to BI. Defined as a technology-driven process for analyzing data and presenting actionable information to help companies, BI encompasses a lot of business intelligence tools, applications, and methodologies. BI is, therefore, an umbrella term and a focused concept. Both business analytics techniques and business intelligence skills are related terms. They are generally strategies and decisions that can help companies across sectors like research and development, customer care, credit, and inventory management. Both of these help companies meet business challenges and use fresh opportunities that arise within the sector.

Business analytics techniques and BI can have far-reaching consequences for the functioning of brands and companies across categories. Some areas they can help impact include critical product analysis, improved customer service, up-selling opportunities, simplified inventory management, and competitive price insights. By allowing companies to understand customers’ and client’s needs in real time, they can help maximize resources effectively and minimize losses.

Business Analytics vs Business Intelligence – Future

But with time, this will become a necessity because social media is a growing platform that no company can ignore, not today and not in the coming years. That is why many corporate companies are now looking at BI programs that can help them not just upgrade their decision-making abilities but also reduce their operational costs and help them use existing opportunities.

Data interpretation and manipulation methods of choice keep changing according to the market’s requirements. That is why companies must be clear about the tools and techniques they use to reach their eventual goal. When companies understand the flexible nature of the economy and their business, it becomes much easier for them to handle these changes through tools that can reach the goal, even in challenging situations.

Conclusion – Business Analytics vs Business Intelligence

In conclusion, Business Analytics vs Business Intelligence both have immense potential, and there are a lot of challenges present in both these sectors, primarily related to the field of technical and social networking. Companies must remember that business analytics techniques are not the same as BI. The requirements of one field are different, and so are the benefits of each of them. A company can only use technology effectively if it invests in it and uses it in a proper and systematic manner. By working with end-users, consultants can help companies use the right tools to use the data to make decisions that will empower the brand and take them to the next level of growth and development.

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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.’”

The Leader In Embedded Analytics & Intelligence

GoodData powers the transformation of decision-making for frontline workers within their daily business processes. The GoodData ​Enterprise Insights Platform​ is a cloud-based, end-to-end platform that gathers data and user decisions and transforms them into actionable insights, delivered at the point of work. This empowers enterprises and software companies to integrate insights into applications so that their customers, partners, and employees can make more intelligent decisions, faster.

The GoodData platform is powered by a highly scalable, elastic, and secured cloud analytics architecture with capabilities that support the entire data pipeline from data ingestion to insights delivery. Leveraging this platform as well as the expertise of in-house data scientists, domain experts, and data engineers, GoodData works with customers to drive a “business outcome focus,” allowing them to finally drive meaningful change for the business.

The Trailblazer of the Company

Roman Stanek, Founder and CEO of GoodData is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was the Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).

The company’s mission is to fundamentally change the way businesses make decisions. Many companies have not seen a return on their BI investment and Roman believes it’s because the insights are not actionable. Companies have expected everyday business users to leave the applications in which they work and look at static dashboards. The business users don’t know what actions to take based on the information in the dashboards. There is good intent behind self-service analytics, but in reality, it’s not delivering the desired results. GoodData’s foundation has always been embedding insights at the point of work.

For businesses struggling to achieve the expected ROI on their BI deployments, people need to understand that embedded analytics is different. “We are embedded into the business process and we are contextual. GoodData is not selling technology to IT. We are selling analytics applications to a business unit for a business problem. GoodData is solving domain analytics problems. By being embedded at the point of work, without having to leave the application in which you are used to working, and because we are contextual, you know exactly what to do with the insights that are being presented to you,” says Roman.

Delivering Exceptional Business Value

Regardless of the vertical, companies recognize the urgency to continuously innovate by embedding real-time analytics into their critical business processes. By bringing actionable insights to everyday business users, they can impact bottom line revenues with incremental changes that add up to immense savings.

Over the past few years, independent software vendors (ISVs) have enabled many businesses to bring analytics into their product offerings. ISVs that embed insights into their solutions recognize the value of partnering with an embedded analytics vendor to bring those insights to their customers faster. They can get to market faster, continuously evolve their products, and meet the needs of their customers faster, all while saving money with no infrastructure costs and not losing sight of their own core expertise.

Driving Innovation with Cutting-Edge Services

GoodData has been extremely innovative in the big data analytics market and recognized over 10 years ago that success will lie with those that embed analytics at the point of work instead of asking every business user to be a data scientist and go out and interpret dashboards on their own. GoodData understood the importance of embedding analytics in critical business processes versus providing a self-service dashboard as recognized in their recent patent award.

GoodData platform empowers customers to gain significant value from their data. By using the platform, enterprises partner with a trusted leader in the embedded analytics market. That leader can help drive the enterprise to success calling upon their expertise having done hundreds of implementations over the years.

Awards and Recognitions

Most recently, GoodData has been recognized as a Leader in the Forrester Wave​™, Enterprise BI Platforms with Majority Cloud Deployments, Q3 2023 as well as a Strong Performer in the new Forrester Wave​™​, Insights Platform-As-A-Service, Q3 2023. Good Data is used by over 50% of the Fortune 500 companies. The company has over 1.2 million users of their platform and over 70,000 businesses use GoodData.

Challenges Faced in Business

When addressing GoodData’s challenges Roman says, “The biggest challenge in my mind is that the industry has been focused on self-service dashboards while we were focused on embedding insights at the point of work. We were going against the popular trend because we knew that people would not leave their work environment to search for a static dashboard to look at visualizations that were difficult to interpret. It has taken some time for businesses and analysts to catch up to understand that the only way to get ROI is to provide the insights with context and help guide everyday users in the actions they should take upon being presented with the data”.

Embedded Analytics is the Future of BI

GoodData sees the future of analytics moving away from self-service dashboards to entirely embedded analytics and believes companies will first move away from self-service analytics and dashboards as they recognize the need to:

1) Embed analytics at the point of work.

2) Provide actionable insights in context.

3) Make the insights more automated and widely accessible.

The next phase will be automating the daily business decisions through embedded machine learning. This will free employees up to spend time on business-critical activities or delve deeper into solving problems that are surfaced through machine learning. GoodData expects more companies will move to use machine learning to automate daily business processes, allowing frontline workers to spend more time on strategic business decisions.

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