business intelligence

Big Data and Business Transformation

During the  time, all new technologies become simpler and more affordable for large-scale use. Now Big Data is going through this phase. As a result, different industries transformation is taking place. Here are some examples of key industries influenced by Big Data.

Retail

In recent years, selling and buying procedures have changed a lot. However, both online and offline store owners use the data to better understand customers, their needs, and comparison with the current offer. This approach ensures an effective operation and allows for huge benefits.

Data analytics is applicable to almost every step of the retail process. By predicting trends, it is possible to determine the demand for a product, optimize the price, determine the target audience, and gain a competitive advantage.

Health care

Big data in healthcare is helping to improve disease detection and treatment, improve life quality and reduce mortality rates. The main Big Data task is to collect as much information as possible about the patient and identify the slightest changes and illness signs at the earliest stages. It prevents disease development, provides a simpler and more affordable treatment protocol.

Financial services, Banking, Insurance

Big Data helps financial companies and banks detect fraudulent transactions. Insurance companies use Big Data to establish fairer and more accurate insurance premiums, improve marketing efforts, and detect fraudulent claims. British insurance company Aviva is offering a discount to drivers for being able to control their driving using smartphone apps and car devices. It allows insurers to observe how safe is driving.

Manufacture

The production process is changing dramatically with the development of robotics and the automation level. Sportswear, footwear and accessories company Adidas is actively investing in automated factories.

In traditional manufacturing, Big Data matters too. With the help of built-in sensors, it is possible to monitor the specific equipment performance, as well as collect and analyze data on its effectiveness.

Education

Now, data is being collected about how people learn. This information is used for new ideas, defining strategies for a more effective learning process, highlighting ineffective areas of the learning process and ways to transform it. In one Wisconsin school district, data was used for almost everything from defining and improving cleanliness to planning school bus routes. The performance data analysis of a particular person in online learning mode leads to the personalized, adapted learning development.

Transport and logistics

There are cameras to monitor inventory levels in warehouses. With the help of data from the cameras it is possible to provide reminds about replenishment. Also, this data using machine learning algorithms can be transmitted to train an intelligent inventory management system. In the near future, warehouses and distribution centers will be almost completely automated and require a minimum of human intervention.

Transport companies collect and analyze data to improve driving behavior, optimize transport routes, and improve vehicle maintenance.

Farming and agriculture

Traditional industries also use data to generate new opportunities. American manufacturer John Deere has applied Big Data techniques and launched several services. They enable farmers to benefit from crowdsourcing real-time data from thousands of users.

Energy

The volatility of international politics complicates discovering and producing oil and gas process. Royal Dutch Shell has developed a «data-driven oilfield» with the aim of reducing the cost of its production.

Hospitality business

Recreational service providers use data to make their customers happier. The main goal is to ensure each room profitability, taking into account seasonal changes in demand, weather conditions, local events that can affect the number of bookings.

Professional services

The professional services like accounting, law and architecture are also changing as a result of advances in data, analytics, machine learning, artificial intelligence and robotics.

For example, accounting software allows to automatically import transactions, track digital receipts and taxes, and automate payroll calculations.

Data literacy defines business success

Now business success is measured by data literacy. More and more companies pay attention to the team literacy, they involve data specialists, organize training and advanced training of specialists. It leads to improved operational processes, performance and outperformance of the competition.

Nowadays, it is difficult to find a company that does not collect data. However, collection does not mean benefits. Many companies are left behind with a huge amount of collected data due to lack of knowledge of how to work with it. Data requires a smart approach. Now information literacy is just as important as reading or writing literacy.

Improving the team literacy level requires some changes and investments. However, increasing literacy is inevitable if the business goal is success and development. Here are a few things to improve information literacy:

  1. Corporate culture

Technology adoption does not fully solve the data management problem. Each team member must have access to the required data. In addition, he must also be able to independently work with the data, analyze it, draw conclusions and make informed decisions. Managers should delegate authority to employees to independently process data and make informed decisions. This model shows each employee’s efficiency and literacy in the company. Otherwise, team members’ literacy level does not matter, since they do not have the authority to make decisions based on data.

  1. Data and data mining technologies

Gathering the right information is essential to being effective and meeting your business goals. Data provides new opportunities, so it must be reliable and meet the company’s request. In this way, data consumers can trust the information they receive, make decisions based on their analysis and receive benefits. In addition, it is necessary to have appropriate technologies for storing and processing data. Tools for extracting data from different sources, processing and visualizing data have a significant impact on the efficiency of the entire data work process.

  1. Data skills

Improving information literacy is now easy enough. There are many different online courses, trainings that will help to acquire the necessary knowledge and skills for productive work with data. Another effective way to improve data skills is to partner with third-party professionals, consulting companies, and so on. DataLabs is a consulting company that provides business intelligence solutions focused on the conduct of effective operations. Collaboration with the DataLabs team will improve information literacy and develop data skills.

Investing in data management is one of the most important investments that business can make. But to get a return on that investment, it’s important to create the right culture, collect and analyze the right data, and help team members acquire the necessary data skills.

Business Intelligence improves customer service

Customer service is always of immediate interest, and it has a significant influence on business activity generally. With the advent of COVID-19 the whole world was convinced how important customer service is. Almost all offline business had to move activity into online and organize here an effective customer service process. Besides the enhanced online functionality, it required communication clarity and simplicity.

Many companies initially understood that the customer support in «such difficult time» would provide them with increased loyalty in the longer term. Now «pandemic disruption» continues, and business success directly depends on its client-focusedness, customer needs and interest knowledge. It is possible to meet these challenges with the help of BI.

The role of BI in business is invaluable. With the help of BI tools, it is possible to understand deeper: business, customers both their behavior and needs, to make reasoned decisions. By combining several information sources Business Intelligence allows to evaluate the current performance efficiency, identify patterns, define possible future problems and prevent them. According to the Gartner’s forecast BI will grow to $29,48 billion by 2022 and it proves again its value.

5 tasks that can be solved by Business Intelligence:

  1. The single source of truth creating

The modern business has many different information sources including ERP, CRM, website, social media etc. Business Intelligence collects data from all sources and visualizes it. In this case data consumer can easily see and analyze the full picture of customer interaction without manual data checking and collection.

  1. Real-time information receiving

To analyze and make decisions in hindsight is ineffectively. It has an impact on customer relationship, competitiveness and relevance. BI tools provide real-time analytics. With the help of advanced technologies data is transferred from different channels and platforms directly on dashboard. It allows to make rapidly well-founded conclusions and respond to customer behavior changes.

  1. Reasoned decisions making

The main BI task is to eliminate guesswork and enable data-driven decision making. Dashboard data allows to analyze and identify ineffective channels, for example by customer conversions. It allows to perform a change to the strategy, forward the budget and get the best result.

  1. Omnichannel

Customers expect their interactions with a particular company to be known to all employees. Addressing to support team client would like to explain his problem once. Aspect defined that 89% of customers are dissatisfied if they need to repeat information again and again. In this scenario Business Intelligence provides all channels view that allows to monitor the full history of the customer.

  1. Churn reducing

Churn rate is one of the main customer service and business effectiveness indicators. Customer retention is always cheaper than new customers searching and attraction. BI dashboards help define in time problem parts that allows to make changes rapidly for customer retention. For example, increasing the time to solve problems, customer support team restricting or training etc.

These are just a few examples of how BI can improve customer service. Depending on business specification and individual needs BI abilities are unlimited. For more information about Business Intelligence possibilities and benefits please contact DataLabs.   

Main BI drivers

Currently data is the «fossil oil» – the main business source for decision making process and future business development. For getting all benefits of corporate information assets it is too important to build proper data governance process.

A brisk growth data storage, data integration and data processing technologies promoted evolution of an enterprise data warehouse into structured abilities set for analysts. At the moment report and analytics ecosystems provide possibilities for technologists to understand better business data consumers problems, work more effectively with end-users’ requests and create customized application.

During the time data consumers became more exacting. Currently users have deeper understanding of corporate data assets. It implies more sophisticated approach from data processing engineers side (data consumers needs and expectations identification, collected requirements synthesis into BI solutions project).

The potential data sources number is increasing that instigates the emergence of the following specialist team tasks:

  1. Metric clarity ensuring

Specialists need to have proper understanding of what information business user needs, structured process and corresponding tools for soliciting and requirements and business terms documenting.

  1. Information sources identifying and managing

Specialists team has to identify corporate data sources that can be used to develop BI solutions and create sources list. Such list will help end-users to define the best information source for analytics application development.

  1. Consistency ensuring

Different information sources can represent the same or similar concepts in different ways. In this case data architects have to harmonize definitions for business terms and ensure consistency in both definition and semantic.   

  1. Maintaining relationship among terms

Business users are increasingly aware of the relationship between different information parts (for example, «client» and «customer» can have the same meaning). Architects’ task to discover such conjunctions, document and manage them.

Data needs protection

On the one hand data promotes huge possibilities for business and on the other hand it demands a huge responsibility from the business. Effective data management system including data collection, storage, processing, accessibility, safety is a necessary part of every company. All system and every its process must be correctly configured and perform its functions to obtain maximum result.

There is no doubt that all data need to be protected, otherwise other business processes can become senseless. News about leak of different companies’ information is not rare. For example, in the result of Yahoo attack in 2013 were compromised 3B of clients’ identity details; Marriott attack involved 500 million consumers personal information; more recent problem – Sephora information leak.

Such situations have negative influence on the whole business space: both an injured business (stock price losses, a barrage of bad coverage, widespread business mistrust etc.) and business that has become a witness of this situation. The management of companies (specifically after the next leak news) is actively involved in cybersecurity issues. However, fear and knowledge lack generate mistaken opinion among management (for example, artificial intelligence technologies implementing ensures protection against cyber attacks).

But before technologies implementing into business processes, it is necessary to pay attention to the following points:

  1. IT department

It is too often situation when one IT specialist plays several roles in the company (from maintaining work computers to ensuring the protection of confidential data). The IT department understaffing and insufficient training level entail negative consequences, including data security issues. There is not the right size of IT department. Its size should be such that all company’s requests are qualitatively satisfied.

  1. Financial restrictions

Every business strives to maximize profit and minimize costs. The management is reluctant to agree to a new cost item of hiring cybersecurity specialists or training existing employees. Trying to save money a choice turns next to a cheaper option – low-quality decision acquisition, that endangers the entire business.

  1. Expenditures for ineffective methods

To choose an effective data protection method it is necessary to understand completely what data the company owns, what information is confidential, where it is processed etc. Without realizing it and chaotically buying data protection tools you may find that all these tools are not suitable for business needs. Money was spent without result.

The process of data management system selection and implementing don’t tolerate haste, «economy» and chaos. Organizing the IT department inside a company it is necessary to understand what functions IT specialists need to perform, what business needs must be met, what goals must be achieved. Only understanding this, it is possible to create effective department. Delegating tasks of data management system implementing to contractors it is important to make a well-considered decision. Trust your business to a responsible partner who helps to achieve goals and improve business performance.

10 Big Data trends that accelerates business development

Technological advance and achievements instigated huge amount of data appearing. Many of us even don’t suppose that we’re producers of them. Every search request generates data and as a result we produce data in a few days more than in a decade in history. Received data has not to be just stored, it has power and effect if it is manipulated. Data is corporate assets that are used by different organizations to improve operative business. The reduction to practice of artificial intelligence (AI), machine learning (ML), IoT and other technologies improved the quality of data-driven business decisions. And it is not a hyperbolic affirmation «data makes business smarter».

Let’s get a view of 10 big data trends that accelerates business development:

1. Accessible AI

Big data that company has can generates value if it is processed using advanced technologies. For effective analyzing companies use: AI, ML and neural networks to forecast. However, to minimize technical readblocks there is cloud sources trend that essentially simplify data access. The hybrid cloud is capable to provide with more flexibility and possibilities to deploy data by moving processes between private and public clouds.

2. Continuous intelligence

Gartner explains continuous intelligence as a design pattern in which real-time analytics is integrated into business activity, processing current and historical data to prescribe actions in response to business events. Continuous intelligence use technologies like event stream processing, business rule management, ML, optimization and advanced analytics.

3. Advanced Analytics

Advanced Analytics is a part of data science. It uses ML and AI to improve analytics across all data life cycle (from the preparatiom method to analyzing). This technology promotes development of business flexibility, rapidly and credible information gaining, data sharing, and cutting time for information extracting and understanding. More detailed information in the entry «More analytics – more possibilities».

4. DataOps and self-service analytics

DataOps is a newish term, but it already is in favor in IT world. DataOps is a technology mix of continuous integration designed to afford actual data for every process member rapidly and fluently. With the help of this companies have a possibility to rise speed and improve data management quality.

Self-service analytics is a kind of analytics whereby users have an opportunity to make data requests and generate reports by themselves. Self-service analytics implementation allows to receive quick and exact result, simplify information sourcing all across chain.

5. Data-backed tools

Currently data for business processes is generated from all sides. The essential accelerator of this became IoT devices influence. Therefore, it instigated problems appearing. The fact of the matter is that data passes a long way to the centralized source.  But technologies allowed to avoid crisis. The edge computing conception allows to hold data in the local storage device near the IoT device for better data management.

6. Smart chat-bots

Chat-bots became an indispensable contact source between the business and consumers. With the help of this tool companies process customers’ requests and establish more personalized cooperation with them herewith reduce a real staff necessity. Chat-bots are based on big data as a connection source needs large data sets to work in the personalized format. Big data is the main source of the information transfer to chat-bots.

7. Intelligent security

One of the biggest business problems is a security threat. Using big data in a corporate security strategy it is possible to get essential profit. As big data contains all information concerning previous cyberattack attempts, phishing attacks, ransomware etc. it is possible to forecast, prevent and cushion an impact of future attempts.

8. Big data as a service (BDaaS)

BDaaS is a suite of big data analyzing methods and cloud computing platforms with the help of which it is possible to manage big data in cloud and provide their access at any time and for every user. Also, BDaaS promotes cost and time saving to deploy big data projects.

9. Dark data

Dark data is a part of big data that stays in the background. It is collected in consequence of specific network operations that aren’t covered by analytics. However, such data can have more value as one can imagine. Also, dark data can create a business security threat. That’s why it’s necessary to recover it or use correctly.

10. Cloud usage

Companies show a high interest in cloud technologies. This may be due to the fact that they able to change management methods of information and technologies business resources, providing their efficiency, security, flexibility, safety, automation, accessibility and optimization.

Main data trends 2021

After 2020 everyone has feelings that the world will not be the same. It was enough restless and astable year for the whole word. But at the same time, it became an acceleration of digital transformation processes. Business and people had to accustom and fit into a new reality. Less digitized companies have become more vulnerable during the pandemic compared to high-tech market players. However, it also became a motivation for such companies to run digital. They had to digitize their processes, upgrade business-models, provide access to data and advanced training for team. COVID-19 has also become a proof that the data plays a big role, and everyone can use it to inform or misinform.

Data science evolves and matures and consequently many organizations try to increase their digital stability and switch over to the data-driven model. Critical important tasks like self-driven car development, protein folding, and algorithmic trading programs have been conducted using data science methodologies and technologies. It’s just a little part of examples. Data science using is much wider, new and improved data science tools will appear in the coming years.

The main data trends and forecasts for 2021

1. Forecasts with the help of data analytics

One of the main 2021 trends will become real-time analytics. According to forecasts, the number of connected to Internet of Things devices will reach 24,1B by 2030. Organizations collect much more data than previously and try to transform it into analytic information that can help to solve business tasks. Real-time analytics transforming data into insights gives a possibility to respond to situation instantaneously.

2. Databases

For the last 40 years companies have histed their databases locally. However, in 2021 and the coming years there will be a trend of databases deploying or migration to the cloud. According to forecasts, cloud databases will grow up to 75% by 2022. It is a reason of different requirements appearing that most likely will come with developing on cloud-native databases, more closely incorporating analytical and machine learning capabilities.

3. Knowledge graph

As the amount of data still grows rapidly, it becomes increasingly difficult to analyze it. Knowledge graph can help in this by closing the gap between human and machine. In the minds of Gartner, it is one of the main data trends.

Knowledge graph has a form of a facts set (description of objects, conceptions and events) connected by typed links. It becomes possible to create a better context for data through linking and semantic metadata. It promotes easy analysis, integration, sharing and data aggregation.

4. Augmented analytics

We generate about 2500 petabytes of data every day and in 5 years this number will grow to 463 exabytes. Data increasing created serious problems in its processing. Augmented analytics can help to solve them. Using ML and AI methods big data transforms into massively smaller and analyzable one. According to the Gartner research augmented analytics will become a driver of BI in 2021.

5. Data Visualization

Data visualization became a perfect assistant in 2020 that helped to understand current situation easier. Creating, critical understanding and evaluation of data visualization will become a fundamental skill for everyone.

Qlik: Gartner's Magic Quadrant Leader

Recently Gartner has published the annual report where gives information about conditions of analytic platforms and business analytics, market and its trends. According to the report, Qlik has a dominant position of Gartner’s Magic Quadrant for analytics and BI platforms for 11th consecutive year.

«Qlik – is a leader in this Magic Quadrant. It has a strong product vision for ML and AI-driven augmentation».

Qlik’s strengths and benefits that were signed in the report:

Full report you can download HERE

Gartner Quadrant Leaders for Analytics and BI: Qlik Sense vs Microsoft Power BI vs Tableau

At this moment in time, the responsibility for analytics has shifted from IT staff to business analysts, database administrators, and data scientists. Thus, BI has gone from generating monthly reports from the record system to interactively creating and sharing forecasts and responding to business questions based on data from an extensive range of internal and external data sources. Before, businesses made decisions in a month. Now, thanks to self-service BI, they can take action in just a few days.

The problem is with the abundance of BI platforms available on the market today, it’s not easy to choose one that will suit your business needs perfectly. In this blog post we won’t be talking about tons of different BI tools but focus only on the heavyweights of self-service BI – Qlik Sense, Microsoft’s Power BI, Tableau. We’ll analyze and compare some of their main parameters to help you decide which one will be the most useful for your organization.

Qlik Sense

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Qlik is the leading visual analytics platform and the forerunner of user-driven business intelligence. Its portfolio of cloud-based and on-premise solutions meet the growing customers’ demands ranging from reporting and self-service visual analysis to guided, embedded, and custom analytics, regardless of where data is stored. Qlik users gain meaningful insights exploring the hidden relationships within data from multiple sources. A fully integrated cloud platform, Qlik is powered by its unique in-memory associative data indexing (QIX) engine. Qlik’s Associative engine identifies and assembles all your data so you can explore without limits. This is easily the most powerful competitive advantage in a data-driven world.

Intuitive visualization and exploration, advanced analytics, and self-service data preparation abilities allow Qlik Sense to combine enterprise governance and readiness enabling organizations to manage the range of BI use cases from a single platform.

Let’s explore some of the key features of Qlik Sense:

Smart visualizations and analytics

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Qlik Sense is not just a visualization tool. It offers advanced and intelligent visualizations that enhance the process of data analysis. The visualizations are interactive, flexible, and adjustable, with the first-rate graphics. There are many great visualizations types available like Distribution chart, Box plot, and Histogram that help users better understand their data. Users can also improve consistency in their application by applying custom coloring filters to data values within the master dimensions.

Conversational analytics and NLP

Thanks to the open API’s, Qlik has integration with the natural language generation & processing, advanced predictive analytics, and augmented intelligence. These capabilities make possible direct data exchange between third-party and QIX engine for calculations. Based on this interface, Qlik has released integrations for R and Python as well. As a result, users can visualize advanced calculations from external tools within Qlik Sense. But what’s more, Qlik users can combine the power of Qlik’s associative model with advanced analytics to support use cases like- sales forecasting, inventory management, and fraud detection.

Data storytelling and reporting

Data storytelling is one of the most powerful and unique features of Qlik Sense. It gives intelligent and in-context commentary on the data analytics visuals. The software creates the entire data story that puts things into perspective and paints a picture that will be presented for the analysis. It’s very helpful for drawing actionable insights from the data. The apps created in Qlik Sense in the form of reports can be printed or exported in Excel, PowerPoint, PDF, or other formats.

Mobility

Qlik Sense was created with the mobility feature in mind. With its native touch interaction and responsive design, it allows you to explore analytics and collaborate with your team using any device. Moreover, you can experience fully interactive offline analysis on iOS, and it also supports leading Enterprise Mobility Management (EMM) platforms.

Scalability and governance

Qlik provides enterprise-class management services that guarantee data safety, control, monitoring, and centralized management. The centralized data monitoring and management are done via the Qlik Management Console (QMC) which controls all the Qlik Sense services centrally. Scalability is another crucial aspect. Elastic scaling in Qlik Sense helps to load and analyze a large amount of data.

Qlik Data Catalyst (QDC) and Attunity

Today’s hyper-competitive environment, where real-time enterprise data is indispensable to success, calls for a more agile approach to analytics and data integration. Qlik fuses Attunity’s data integration solutions with the data management and cataloging solutions of Qlik Data Catalyst and offers users an enterprise data integration platform to convert their raw data into a governed, analytics-driven information resource. This platform encourages the movement of data in real-time across multiple cloud environments and data lakes. When combined with predictive analytics and AI, it scales real-time insights throughout an entire organization.

Qlik Insight Bot (QIB)

Qlik Insight Bot offers a fast and simple way to ask questions and discover insights to make data-driven decisions just by having a conversation. With each question, the Qlik Insight Bot engages users and instantly brings up relevant charts and insights, including key drivers, comparisons, predictions, and more. Its self-learning AI makes the system progressively smarter.

Microsoft Power BI

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Microsoft released its data visualization tool to the general public in 2015 under the name Power BI. 4 years later Gartner recognized Microsoft as a leader in “2019 Gartner Magic Quadrant for Analytics and Business Intelligence Platform” due to Power BI’s capabilities. Presently, Microsoft offers Power BI as a part of the Office 365 suit which contributes to its popularity even more.

It’s easy to understand why equally small and big business analysts adore this tool. Business users who have comprehensive knowledge of Excel intuitively understand Power BI. The same goes for technical users who are likely to adopt Microsoft Office stack. In terms of building and display, Power BI is a nice tool that offers visual data discovery and data preparation along with interactive dashboards and augmented analytics.

You can choose between Power BI Reporting Server (PBRS) option or a SaaS option in the Azure cloud in Power BI Report Server. It’s user-friendly and doesn’t require much time to create and share reports. The cloud option doesn’t entail infrastructure support but if you plan to deploy it on-premise, naturally you’ll need support no matter the size of your organization.

Now let’s take a look at some of the prominent features of Power BI that have made it a leader in Gartner’s BI Quadrant.

Appealing visualizations and robust data modeling

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One of the main features of Power BI is the ability to personalize information dashboards according to your business needs. You can easily embed BI reports and create an interactive in-depth analysis for powerful data storytelling that will inspire your business users to embark on their data discovery journey. There are in-built visuals and custom visualizations created by the community available. After deciding on perfect visualization, you can get down to drill-ups and drill-downs.

As for data modeling, Power BI has a query editor. It’s an integrated ETL tool that allows you to work with data from numerous data sources to build effective data models. It is powered by a Data engine that can deal with complex enterprise use cases. In addition, Python and R that are integrated with Power BI open the door for advanced analytics, visualization, and data mining.

Smooth integration with applications

Regardless of your Microsoft product being deployed in cloud or on-premise, the integration with Power BI is smooth. It integrates flawlessly with legacy as well as modern enterprise applications and perfectly visualizes available data. Power BI has different in-built connectors that easily connect and join multiple on-cloud, on-premise, and streaming data sources to use it for data preparation. Furthermore, it supports integration with Cortana, Excel, and many other applications and services.

Fresh insights with DAX

DAX (data analysis expressions) is a collection of functions and operators, that can be used for data analysis and calculations. Microsoft developed this functional language to interact with data in Power BI, PowerPivot, and SSAS Tabular. Although for some DAX concepts may seem simple, DAX itself is quite powerful. With it, data analysts can create new measures and obtain unique insights into data through exclusive visualizations.

Promising product roadmap ahead

Since Microsoft’s Power BI is a relatively new product on the BI market, it can be improved even more. Even now, new features and enhancements are available every month. Microsoft monitors users’ requests about the features and prioritizes those that are in high demand. Regarding licenses, Power BI has several – Power BI Desktop (free), Power BI Pro, and Power BI Premium. There are also many Power BI communities that help users find solutions and suggestions to a variety of problems.

Tableau

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Founded in 2003, Tableau is an enterprise-scale business analytics platform with self-service capabilities. Tableau’s dashboards present organizations with an interactive interface that comes with a depth of colors, visualization options, and a robust analytics engine. Users can combine large data sources and access them instantly without the need to rely on their IT team. You can securely access Tableau through native applications even on Android and iOS.

Tableau’s popularity is accounted for by easy deployment, support, and usability. It’s a tool focused primarily on data visualization so if that’s what you’re looking for, then Tableau may be your pick. Let’s take a moment to consider what it has to offer.

In-depth insights

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With Tableau, organizations can analyze data even without any particular goal in mind. This method of exploring and visualizing data from different angles may lead to sudden useful realizations and insights. You can frame ‘what if’ queries and operate data hypothetically visualizing it in different and dynamic ways by adding components for comparison and analysis.

Engage different data sources

While analyzing, you can pull data from many sources like cloud, spreadsheets, data warehouses, etc. Tableau’s data blending feature will help you blend all the data in the visuals while keeping them apart at the source. This method simultaneously accelerates the process of combining data from multiple sources and negates the need for priming data before processing it for the visuals.

Real-time data analysis

Data is changing fast so it’s important to keep your dashboards always updated. Tableau gives you the ability to make live connections with the database. When there are new entries or changes in the database, you can refresh in Tableau and see all the changes reflect on your dashboard. It’s also possible to set specific time intervals for the data to refresh. This will help you draw valuable insights in real-time.

Drag-and-drop clustering

This may be the most powerful feature of Tableau since it allows you to combine similar group members. “How is it helpful?’, you may ask. With grouping, you get statistical information that will help you see similarities between different groups as well as compare their performance for segmentation analysis.

Tableau Online

Tableau Online is a fully hosted cloud solution that offers immediate setup and efficient scalability for enhancing business growth. It’s a platform where you can publish and share your discoveries with other people. You can invite customers or teammates to further explore hidden opportunities using interactive visualizations and accurate data.

Conclusion

By and large, all these tools work following different principles. Qlik Sense is more than a visualization tool. It has a strong and unique ETL engine and connectors that can connect you to any kind of source system. Power BI is a fantastic asset for quick insights, great user experience, and collaboration within your company. Lastly, if you want a simple interface and beautiful visualizations, Tableau might be your preferred choice. Usually, discussions may come more around commercials or individual preferences, but if it is an end-to-end BI and you don’t have anything for your ETL or Data Warehouse, then Qlik may be a better fit.

Qlik makes cloud analytics more accessible to every customer

Qlik announced new packaging and adoption programs that will give customers more options and make cloud-based analytics simpler and more cost-effective to adapt. These programs comprise new packaging of Qlik Sense Enterprise with SaaS only as well as Client-Managed options. Additionally, QlikView customers can easily adopt Qlik Sense Enterprise SaaS and host their QlikView documents in the cloud at the same time.

James Fisher, the Chief Product Officer of Qlik stated, “Customers are eager to leverage the scale and cost efficiencies of analytics in the cloud, and at the same time leverage augmented and actionable analytics to turn insights into action.” He added that with their latest Qlik Sense offering and new Analytics Modernization Program “it’s easier than ever for every Qlik customer to adopt and leverage cloud-based analytics and benefit from new AI and cognitive technologies across their entire organization.”

In the second quarter of 2020, Qlik customers will be able to coordinate the deployment of Qlik analytics with their IT strategies more effectively via two options, SaaS or Client-Managed. Those who choose Qlik Sense Enterprise SaaS will reduce management issues and minimize infrastructure costs by deploying exclusively in Qlik’s cloud. Meanwhile, customers who go for Qlik Sense Enterprise Client-Managed can deploy either on-premise or in a private cloud depending on their governance or data requirements. They can also license both and make the most of Qlik’s unique multi-cloud architecture.

Qlik’s Analytics Modernization Program will further provide QlikView customers with expanded flexibility and choice. It allows them to adopt Qlik Sense steadily, at their own pace without disruption to existing QlikView operations.

Steph Robinson, Qlik Manager Business Intelligence IT at JBS USA said, “We’re excited about the growing adoption of analytics we’re seeing in our employee base with Qlik Sense”. He noted they continue to leverage QlikView apps that have been already created but also give their developers an opportunity to adopt Qlik Sense at their own pace. “Being able to leverage our existing QlikView apps, while also extending analytics capabilities through Qlik Sense, has accelerated our journey to modern BI and is helping our organization become more data-driven”.

The Analytics Modernization Program opens up the following possibilities for QlikView users:

Doug Henschen, VP and Principal Analyst at Constellation Research pointed out, “As organizations increasingly migrate applications and data to the cloud, they look to maximize the value of that data to drive strategic advantage.” He continued saying that “by providing these new options to move analytical workloads to the cloud as quickly and easily as possible, Qlik is responding to growing customer expectations and where we see the industry headed.”

Other exciting developments Qlik Sense customers should look forward to are various new features in the April Qlik Sense release that will help them broaden analytics adoption through the cloud. Among new elements, there will be new visualization and dashboarding enhancements, the ability to share charts, notifications within the management console, and improved data file management and data connections for data flow into individual Qlik Sense workflows.

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