Nowadays, data insights as a service can harvest raw big or historical data by aggregating hundreds of different data sources. Moreover, Data Insights as a Service can analyze this data through business expertise which is configurable to the given business. This service interprets the data insights to transform them into levers directly actionable by sales representatives. These levers can be used for example to initiate, deepen, or "close" a commercial relationship.
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It could be said that data insights are a piece of developed customer knowledge. Passed through the lens of Data Insight as a Service, a raw deal becomes an insight: a piece of customer knowledge relevant and directly usable by salespeople.
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So the main task of analytics experts should not be the "preparation of reports at the request of the business", but the preparation of an environment in which the business can independently receive answers to its questions.
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If you invest in making data visualizations available to business users, the quality of data insights, and the quality of decisions made within the company, will increase. 
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What are the differences between data, analytics & insights?

Let's define key terms, without any context. This will help you work with data insights for your business.

• Data is information that is gathered by measurement, observation, logical or mathematical processes and parsed into a form appropriate for long-term storage, transmission, and (automated) processing. The data can take many forms such as transactions, product information, user behavior and so on.
• Data analytics is the process of obtaining data insights by studying data visualizations. We can combine this with the initial data "polishing" process. This is the process of transforming the received information into useful elements for the company. Data insights themselves are of little use if they cannot be analyzed and turned into useful information.
• Data insights are data that has been evaluated and put into a useful perspective to give the salesperson directly actionable operational levers. In addition, data insights are suggestions for action based on a strong pre-purchase signal.
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Since we are already comparing these categories, it is worth mentioning the types of data insights that are used in analytics will be useful for your business intelligence. There are two types of data analytics and storage:
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• Structured data insights are characterized by the fact that they do not change over time and are available for quick use and analysis. These data insights are stored for comparison in the following months to generate statistics.
• Unstructured data insights: Unstructured data is characterized by the fact that it is constantly updated, so it is not ready for analysis and requires additional work to use.
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Data insights allow us to conduct better analytics to understand the consumer, and how the market and everyday interests are changing.

Business Intelligence and data analytics in insight gathering 

Let's go further and talk about one of the most important tools for business in working with data, Business Intelligence. Business Intelligence refers to a set of technologies that enable businesses to facilitate decision-making. Careful collection and analysis of data insights allow professionals to have an overview of all areas of their business activity.
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It is thanks to data insights that teams can identify problems and historical trends and, having done, use these insights to improve the internal and external management of the business. All these operations are made possible thanks to software specially dedicated to Business Intelligence.
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Business Intelligence software combines a wide range of tools for data insights, visualizations, and applications. All these analytics create reports, tables, and visualizations on a special platform. Business intelligence makes it possible to classify data insights, making them more understandable and readable for business purposes.
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Originally used in the field of information technology, Business Intelligence has now become highly democratized intelligence. Nowadays, obtaining data insights has become important for many companies that want to improve their intelligence.
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There are specific key differences between storing data insights and Business Intelligence. However, before diving into data insights, it is essential to note that they operate in the same space and are equally important to the overall Business Intelligence strategy.
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Below are some of the inherent differences between them.
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• The main purpose of Business Intelligence is to analyze data insights and present useful information to decision-makers. Here, the repository of data insights is a centralized repository for collecting, processing, and storing data from many disparate sources.
• The purpose of Business Intelligence is to help business users make smart business decisions based on the use of data insights. Business intelligence can also improve forecasting and analytical skills. The goal of a data insights store is to store data visualization in a central location so that Business Intelligence users can access a holistic view of your business data insights.
• Business Intelligence output consists of dashboards, reports, data insights, visual effects, charts, and graphs that contain information and trends of business intelligence. Providing structured access to these results allows business users to make sense of complex data insights. That is, business intelligence is a source of data insights stored in tables and dimensions of data insights models.
• Business Intelligence users are typically C-level executives, managers, or data analysts who seek to analyze data insights promptly for better decision-making. Conversely, some companies are often managed and supported by data insights architects and engineers who provide business users with data insights that are ready for analytics. 

What might include data insights?

As we have seen, data insights are economically useful for the company and the brand. But working with this resource as part of a business is quite complex. Working with data insights begins with observing and identifying the main problems of the target audience of your business. And this process of working with data insights ends with the brand's response to reducing obstacles and increasing the motivation of the target audience. This approach to working with data insights can be structured as follows:
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• Observation. At the very beginning of working with data insights include a desire, a feeling, and intelligence. This arises from observation or based on consumer experience.
• Motivation. This is addressed to a business client who sometimes seeks to rationalize irrational behavior.
• Brake. This part includes inhibitions, risks, and fears, as well as personal guilt, and social pressure (including peer groups, and family…)
• Business response: Pitch, promise, positioning... The strategic planner can here mobilize technical and factual arguments, place, historical period, pedigree legacy, and duration of action, and rely on approving the name of its founder, especially when the business is of the same name.

Data insights examples

It is important to note that in addition to quantitative data, some companies may wish to use auxiliary data insights to assist in decision-making. Supporting data insights are quantitative and have already been collected by a third party for various purposes. For example, a company may rely on data insights from the US census to make decisions about its marketing campaigns. In the media, a news team may rely on government health statistics or medical research to inform content strategy.
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A good example of how insights can work for the benefit of a business is the analysis of Crazy Egg. One day Crazy Egg decided to abolish the free plan for new customers. This measure allowed an increase in revenues for a while, but in the long run, it was not the best solution and reduced customer percentage. After analyzing consumer data and using actionable data insights, the company decided to re-offer this tariff. According to the business owners, if they started a SaaS service today, they would never touch the “free” plan.
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In any good data model, it is important to fully understand the essence of data insights and to determine how to get exactly the information that is needed to help your business grow. By knowing your data insights well, you can use business intelligence wisely.
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Today many services help businesses with the analytics of data insights, moreover, there are more and more offers on the market for easy-to-use tools. These tools simplify the process of auditing and analytics. Your business needs such actionable data insights to problem solve and cooperate with your customers at a higher level.
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Thanks to the verification of huge volumes of data insights, both small and large businesses that have access to analytics can quickly make raw information useful. This evidence-based information can help their clients make more effective business decisions. A professional service can help you benefit from your audit projects using actionable insights. Starting from the measurement of compliance and the strength of internal control and ending with the detection of fraud.

Define business objectives

There is no doubt that companies need to define business objectives and put data in context before working with data insights. The most widespread business objective is always to work with clients. When it comes to working with customers, companies like dataforest.ai usually have access to a significant amount of insights through analytics. But conducting more in-depth analytics usually requires more time and a larger budget.

With the help of data insights analytics, companies can use powerful and automated analytics tools to help dig deeper into data insights without requiring significantly more staff time. This will enable you to provide users with valuable operational data insights like never before.

Data collection

Today, with web analytics and data insights tools, companies can gather comprehensive data insights from mobile devices, website traffic, server activity, and other sources relevant to the project.
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Mechanisms for the collection of data insights develops at the pace of technological change. Recent advances in mobile technology and the Internet of Things are forcing companies to rethink how they collect, analyze and monetize new data. At the same time, privacy, regulatory and security issues related to the collection of data insights are increasing.

Data integration & management

To collect and then analyze data insights to obtain useful information the specialists at dataforest.ai need to combine raw data insights with metadata into a data lake. From there, actionable data learning, and artificial intelligence programs use sophisticated algorithms and analytics to find trends and patterns.

Data analysis

Data insights and various actionable data analysis models are used depending on the business problem that the company intends to solve. Based upon these tools, actionable data will be formed to obtain an understanding that answers a specific question.
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Data insights analysis models are:
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• Descriptive analytics: The goal of descriptive analytics is to answer the following question: "What is happening in the company?". Studying actionable data makes it possible to describe what happened in the company at a specific moment, and from there it will be possible to determine the right path or strategy to follow.
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• Diagnostic analytics: This type of analytics of data insights answers the question "Why is this happening?", for example, what caused the advertising campaign to have such an impact, or what factors contributed to the decrease in sales?
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• Predictive analytics: Analytics used to identify insights to answer the question "What could happen?" To achieve this, algorithms are generated and technologies such as machine learning are used to predict what may happen in the future.
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• Prescriptive analytics: Insights to help set future action plans by answering the question "What should I do?" Achieving this requires a large amount of actionable data and specialized technology, and it is also the most difficult to implement due to the investments that the company must make to implement it.
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Usually, processing the analytics of a huge amount of actionable data takes a long time, during which your business competitor will have time to take the business to the next stage of decision-making, but we can help you avoid this situation. This is why you need to implement new analytics tools for working with data insights and actionable data more than ever.  Actionable data analytics allows you to assess the current visualizations of the business as quickly as possible in real-time in some situations. Where a business once collected actionable data to use business analytics from data to insights, now organizations analyze actionable data to make immediate decisions. The ability to act quickly with actionable data gives a business significant advantage.

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Let's further analyze how to get useful visualizations for analytics from the data visualizations, which will lead to impactful & powerful insights.

Use a data visualization platform to display information

The easiest way to work with analytics data is through data visualization. 
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Decide what you want to say with the data visualization. A story is needed. The graphs only help to tell it. Do not complicate the data visualization, remove everything unnecessary, choose a colour scheme, and place accents. An example of a good data visualization will include most of the following data criteria:

• There are accents on large numbers.
• Only 4 primary colors are used.
• There is important analytics by region, products and services.
• All graphs are signed and have additions.
• It is easy to switch between levels of data visualizations.
• It is easy to filter the view for specific areas of interest.

Find the appropriate patterns in data sets

If you are working with numerous data insights visualizations it is impossible or impractical to connect to visualizations directly. Prepare work tables using SQL, Python or R. Exclude unnecessary segments, limit the period, check if there are any empty values, incorrectly recorded data insights, unexpected extremes.

Look at the right periods

Collecting data insights and actionable data has many purposes, especially for analytics or research activities. That's why it's essential to consider time frames when working with data insights and data visualization. For instance, it is obvious that if you take into account only data insights obtained during the economic decline of your business, then analytics based on this will turn out to be wrong for calculating the average statistical performance of your business.

Avoid gauging averages and totals

When working with large volumes of data insights and actionable data, you don't always need to see all ranges. By aggregation, hiding certain rows or filtering actionable data in your analytics results, you can make full data visualization tables easier to understand.
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Summarizing, it is possible to simplify analytics with tables and actionable data to visualizations only by basic data insights. Thus, you avoid averages and totals but get relevant analytics and actionable data for your request. However, hidden data insights are not lost, in many applications, they remain part of your workspace, so they can be used to perform other calculations of actionable data that will be useful to your business.
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Data insights are a reliable tool for improving your business. To receive analytics about the company's services and products, you need to test them. This allows you to evaluate the experience that the consumer received from the product.
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The company needs to conduct such analytics at all stages of the creation of a product or service — from creation to launch of sales. Working continuously with data insights will make it possible to eliminate problems in their early stages, which significantly saves time and money. 
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Advantages for a business of product research by a real consumer:
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1. An objective and honestly reported user experience will resolve many internal company disputes;
2. The ability to analyze analytics and the behavior of users with the business, resource, etc.;
3. Identify solutions to product/service problems;
4. Knowing the shortcomings thanks to data insights, it is possible to improve the analytics, thus stimulating the demand for it.

For example, a young mother who regularly searches for children's products will start to receive advertisements on social media for relevant brands. When searching for an interesting gift idea for, say, a colleague, the algorithm according to metrics will display ads for several suitable options based on data about your place of work, age, and so on. Thus, the time required to capitalize on new opportunities and marketing budget costs is minimized to near zero.
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Stages of service/product testing:
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• Identifying the specific service or product to be tested;
• Setting the goal and objectives of the research;
• Formation of clear criteria and analytics for each of the tasks - in which cases it will be considered solved, and in which cases - not;
• Drawing up a research plan and analytics in order not to deviate from the scenario of working with the user;
• Conducting business and actionable data research;
• Discussion of the product with the participants — obtaining objective data visualization about the pros and cons, and features of using the research subject;
• Identification of problems, assessment, making recommendations for product improvement;
• Drawing visualizations and formulating analytics for improving the business.
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As we have seen, the combination of working with data insights and actionable data can achieve very promising analytics for businesses if implemented correctly. Such well-targeted knowledge of data insights as well as actionable data can lead to the creation of sustainable and effective strategies for your business. Among them, the following can be distinguished:
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• Have a deep knowledge of the client. You will know all actionable data by what your customer buys, what they like, and their location, if there is a need for product improvement, or if there is a need for new products.
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• Information will allow you to adapt your product according to the needs of the customer, without analytics and neglecting the sustainability of the product, which will lead to the implementation of marketing actions, according to which the consumer will feel that your company, in addition to meeting his needs, brings added value to his life and the environment. 
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• You'll be able to improve your analytics and customer acquisition channels, understand the impact of your advertising campaigns, strategies you can implement to retain them, and how to influence their purchasing decisions. Being able to align these insights with your organization's sustainability goals through data insights and actionable data is critical. Large companies are capable of generating prescriptive analytics framework strategies to create data visualizations and use these to influence their users' decisions with data insights.
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• You can analyze delivery routes to find out what is most convenient for transporting your product and what has the least impact on the environment, in which time slots your product has more visibility, and to determine which strategies to implement to achieve better prices, and raw materials to optimize the sustainability of your business, among other things.
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The evolution of Big Data has made it possible to establish deeper and more detailed knowledge of customers and supply chains. By adding the use of technology and learning algorithms, it is possible to create increasingly accurate predictions about consumer behavior.
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This capability allows more and more companies to anticipate and generate strategies to manage situations that may be unfavorable. Data insights and data analytics are undoubtedly fundamental tools that are becoming increasingly indispensable in the commercial world.
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What is the purpose of data insights?

Insight in business is a combination of data from different sources which helps a company find the best solutions and can help determine the profitability of a product or service that can affect the entire course of work.

How to Get Data Insights?

To get insights, you need to:

• Analyze business processes and company goals.
• Implement a sustainable Data Analytics capability.
• Research, formulate, and test hypotheses, conducting A/B tests.

Why Are Data Insights Important?

Organizations use data analytics to discover and prioritize new product features. They can provide greater specificity of client needs, produce more features in less time, and launch new products more quickly.

What is Business Intelligence?

Business intelligence is largely concerned with gathering and processing data, researching a company's performance, analyzing this data to uncover trends, patterns, and core causes, then making business choices based on insights-based data.

How are Data Insights related to BI?

Business intelligence uses the big picture approach, using the insights gained from data analysis to examine what has happened in the past to inform future decisions.