data analytics

Data as a key element of a decision-making process

Data is an integral part of modern life. Almost every human action generates large data amounts. The most valuable use of this data is how companies use it to make business decisions. For example, viewing candidates’ profiles on LinkedIn to recruit a targeted candidate, research and identification of priority markets for product promotion.

The most serious business data applications are automated and used to solve more complex and important tasks.

These processes take place automatically without human involvement.

Experience and intuition are traditional assistants to business leaders. However, despite the value of these qualities, a business using data in the decision-making process is 19 times more profitable. Data helps to make better business decisions that leads to goals achievement.

Many companies claim to be data-driven because it is «trendy» these days. But in practice this is not entirely true. They only take data into account if the data matches the beliefs and intuition of the business leader. A data driven business assumes that data is the only point of truth. Decision making of any complexity occurs as a result of data analysis.

There are 4 main areas where data is needed to make effective decisions:

  1. Solutions related to customers, markets and competitors

The data will help to understand better customer behavior, track changes in habits and interests, make a targeted offer, meet customer expectations and stay ahead of the competition.

  1. Financial decisions

The company’s management has the ability to investigate in detail sales trends, cash flow cycles, profit forecasts and changes in stock prices. This allows to make informed budget allocation decisions and leads to cost savings and growth.

  1. Decisions related to internal operating activities

The joint use of data and Artificial Intelligence allows to optimize the operation of equipment, set up the process of preventive maintenance. This will allow to determine in advance where breakdowns may occur, and repairs will be required. With this information it’s possible to plan the optimal replacement / repair process and minimize deadtime.

  1. Solutions related to human resources

The data helps to study the team composition and quality, to determine the shortage of certain specialists, qualifications level, the appropriateness of compensation for a certain type of work, since employees are always tempted to go to a competitor, taking their experience and skills with them. So, using the data, Google identified 8 basic qualities of a good manager, including «a good coach», «a clear vision of the team». This analysis allowed the management of the company to make informed decisions on the promotion of employees on the career ladder.

Machine Learning & Big Data

Among other modern terms and concepts, the most relevant are machine learning (ML) and Big Data. These 2 terms are often used in conjunction, although they have a fundamental difference. And it is important to understand this difference during a data strategy development.

The similarity between machine learning and Big Data is that both terms refer to the field of theoretical academic research and practical data-driven business applications. It is a scientific discipline that studies information and use cases.

Data is the main engine of technological progress. It helps to create new tools and platforms to change the world through analytics, more accurate modeling and forecasting. The development of the Covid-19 vaccine is a great example of the data importance in today’s world. Usually, it took up to 10 years to develop a vaccine. However, over the past decade, the ability to collect and process data has expanded significantly. It has significantly accelerated the pace of vaccine development. If this pandemic had happened in 2010, it would have taken a lot longer to solve this problem, just because technologies for deep data understanding were in their infancy.

This situation is made possible by both Big Data and Machine Learning. Let’s make sense of the terms.

Big Data is a collective term that includes a huge amount of ever-growing information, as well as tools, methods and technologies that have been developed to work with data, including Machine Learning. With the Internet transformation into a daily use tool, Big Data has begun to be identified as a powerful tool. Big Data isn’t just about size. Data definition as big assumes the presence of 3 characteristics («3 V»):

Machine Learning is a type of computer algorithm. It can be viewed as part of Artificial Intelligence (AI). A fundamental aspect of intelligence is learning. Machine learning is involved in creating programs that help to perform better taking into account an ever-growing data amount.

It is important to understand the difference between supervised and unsupervised ML. Supervised learning is a Machine Learning technique that includes tagged learning algorithms that lets you know immediately how well an operation has been performed. Unsupervised learning is a method of Machine Learning, as a result of what the system under test spontaneously learns to perform tasks.

Big data and Machine Learning are intertwined. The best results are most likely to be obtained by using the most appropriate ML and Big Data processes.

However, if the business does not work with Big Data, Machine Learning is unlikely to be needed. Its main advantage is the extraction of value from datasets that are difficult for classical computer and statistical analysis. For example, for a static dataset that fits into an Excel worksheet, the ML implementation will not be justified. It is advisable to use this tool in the case of working with unstructured data that cannot be understood using tables (text, graphic, sound data etc).

What’s the best way to start interacting with data?

High quality interaction with data is the market leaders’ hallmark. Data has become the foundation for groundbreaking concepts like Artificial Intelligence and the Internet of Things. The main goals of using data: products and services upgrade, internal processes optimization and performance improvement, understanding user needs and behavior, monetizing and additional revenue generating.

Everyone understands that it is necessary to work with data, but not everyone understands where to start. Any process should start with setting goals. Before you can start working with data and getting value, it’s necessary to establish long-term and short-term goals for the company (for example increase profits, scale, reduce customer churn and manufacturing defects, understanding customers and the market). Moreover, each team member must know and understand them.

Here are a few guidelines to help you identify a company’s capabilities using data:

  1. Use cases

An effective solution would be to familiarize yourself with existing cases and look at the other companies’ experience, how they used the data and what result was achieved. A great example is the American company Netflix, which has adopted data mining. The company uses the collected data on the behavior of their customers to form recommendations for films and shows, content, etc. With the help of data company monitors the quality of video playback and it helps to increase the customer service level. Also, Netflix monetizes the received data through advertising partners.

  1. Brainstorm

To solve the problem, it is necessary to gather all interested business participants and brainstorm the way. The purpose of this process is to combine business goals and possible use cases of data to achieve a result.

During the brainstorming process, it’s important to answer the following questions:

Top 6 Benefits of Business Intelligence Usage

Business intelligence is a powerful tool for the modern business. Companies and organizations in different activity fields generate a huge data amount. This tool is a set of qualitative methods for obtaining the values ​​of the collected data. Business analysts’ task is to process, interpret and analyze information. Action and plan development, informed decisions making, current situation seeing – the result of business intelligence.

The main benefits of business analysis for companies:

  1. Informed decisions making

Making strategically important decisions such as planning a marketing budget, predicting the most popular product or service, determining keywords for promoting a business is not complete without business intelligence. The tool provides an informed decision-making process, thereby improving results.

  1. Increased efficiency

Business intelligence helps to identify all the shortcomings in the course of a specific task. This allows to reduce time, energy and resources costs which, as a result, leads to the optimization of all business processes.

  1. Budget increasing

Small businesses have much less financial resources than large businesses. This tool usage helps to learn more deeply and understand customers’ behavior and needs, to gain competitive advantages. This leads to the maximization of budgetary funds.

  1. Goals achievement

Every company must have short and long term goals. Data processing strategy and business strategy are built is consideration of these goals. Data visualization allows to track past and present performance information against KPIs. Detailed and easy-to-use diagrams, tables, graphs greatly simplify the decision-making process. This, in turn, leads to the achievement of business goals.

  1. Revenue increase

Many companies that implement analytics technologies into operations have already benefited and experienced significant revenue growth. McKinsey (an international consulting company that specializes in solving strategic management problems) conducted a research. As a result, it was found that the average increase in the income of organizations that invest in big data is 6%.

  1. Relevant information possession

A large number of proposals on the market contributes to the dynamic change in customers’ interests and needs. Analytics provides a detailed description of the target audience, their needs and actions. Companies can correlate product and services with analyzes and determine the relevance of their offerings, upgrades and new developments.

MDM implementation requires a clear strategy

Digital transformation has forced management to rethink current business models to accelerate the digitalization process and update analytics tools. However, this process is not fast.

Now, data collection and management are a usual business practice. Despite this, there is a high probability that the data is scattered, fragmented, and uncleaned. Inaccurate data and flawed data management can play against business and hinder effective decision making and development. Therefore, this leads the company to poor performance indicators.

Master data management (MDM) plays an important role in enabling intelligent business processes (providing datasets with the correct structure, hierarchy and management). MDM manages critical data across multiple sources, channels, and departments. Master data management implementation requires a well-defined strategy. Let’s look at several important steps in developing a successful master data management strategy.

  1. MDM clear objectives setting

The master data vision must be consistent with the whole business vision. This contributes to the success factors identification and objectives achievement in functional, technical and financial terms. First, the MDM economic model should answer the questions “Why?”, “How?”, “Who?”. This will help identify business pains and data problems. Solving these problems at early stages ensures all business stakeholders support and approval.

  1. Focus on a holistic approach to master data management

Using a multiphase approach to an MDM strategy can be more effective by working with a minimal set of phase objects and scaling it into the next phase. Ignoring such a detailed model when building an MDM solution further can lead to the master data creation from isolated and disparate sources.

  1. Determining the most relevant implementation style in accordance with the existing IT architecture

Companies should clearly define their target architecture, existing technologies, and select a system integrator. Effective MDM technology must support real-time analytics and operational processes to align with the overall the organization’s IT architecture and ecosystem.

  1. Data management rules defining

Business owners need to manage data across all processes and departments. The effective master data management process should identify, measure, record and correct data quality problems in the source system. A formal data governance model should include detailed business rules, governance mechanisms, controls, and data compliance.

  1. Implementation with a strategic plan

The strategic plan can demonstrate the steps implementation in accordance with the business objectives. This prevents MDM solutions from failing as a result of structural flaws that damage the entire data system.

  1. Stagewise ROI verification

First, it is necessary to determine the parameters and indicators that determine the data management success throughout the entire life cycle. MDM stakeholders can be from different organization parts and have different goals. In that situation, it makes sense to check ROI in stages. For example, when custom domain is implemented into a strategy, you need to check your ROI in terms of increasing cross-selling, sales, etc.

  1. Tracking results after implementation

The MDM strategy requires analysis before implementation and monitoring afterwards. All employees, company’s management and stakeholders must work together to achieve their business goals.

  1. Regular improvement

All company staff must be trained in how to format, enter, store and access data. Regularly checking configuration, installation, data models, data management tools, hierarchy helps to avoid problems.

More capabilities with real-time data analytics

If we compare modern business to a car – data will be the fuel material for its successful work. Every day companies have to collect, process and analysis a large volume of data and consequently it’s not always possible to make answer instantly. Real-time data analytics remove time lags between information collecting and processing and gives a ready answer here and now. It helps to improve business operations.

Gartner defines real-time analytics as «symbiosis of logic and mathematics that applicable for data to provide understanding and quick effective decision making. Sometimes analytics completes during some seconds or minutes after new data income».

Accuracy and speed are the determinative of data analytics. And modern business space needs such tool for rapidly information providing that helps to minimize costs, cut downtime and improve business decisions.

Real-time data analytics benefits:

1. Quick and effective business decisions

The main task of real-time data analytics is business information rapid receiving that is used for strategy and decisions improving/correcting, problems uncovering and fast response to them. Currently business demands rapid data processing and momentary answers. This tool provides such opportunity.

2. Operatiional processes improving

Real-time data analytics promotes better understanding of the working structure and recourses using. Hence, it allows to plan working processes rationally, reduce costs, track staff performance, identify possible weaknesses and improve operational processes.

3. Focus on customers

Customers are the main element of the banging business. «Satisfied customer = successful business». Reputation of the company creates with the help of customers. And as their needs will be met so the company’s reputation will improve. That’s why improved customer experience will promote business growth and performance. Tracking consumer behavior online and service customizing according to their preferences can be made by using real-time data (for example product design changing). Also real-time data collecting and analysis play an important role in customer interactions in contact centers and allow to know customers’ calls history and reduce the time gap.

4. Errors and frauds minimizing

Reduction of errors amount and operational efficiency increasing directly depends on using of real-time analytics. The ability to evaluate data every second enables organizations to respond to errors and problems in plenty of time. This toll integration into the security system enables early detection of frauds. Regular monitoring of the operational space allows identify suspicious movement, theft and hacking.

5. Agility and revenue enhance

As real-time analytics enables to make more efficient business decisions so business ability is increasing. Real-time data helps to identify and predict problems and risks that ensures smooth functioning of the company. Hence, revenue and market value are growing.

Quick expansion in automation and IoT connections will result in an exponential data growth. Consequently, real-time analytics will be indispensable and useful business tool across all sectors to improve outcomes.

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

The role of Data Analytics in organizations’ activities

If we’re talking about critical business tools  ̶  without dispute one of them will be data analytics, which works with huge volume of data. It’s too necessary for business to make meaningful insights, but not only collect and analyze data. High quality and proper data analytics project could create a clear picture of your actual point, past point and your future direction of development.   

We’re living in the data era and almost every task is solved by analytics and it doesn’t depend on any kind of answers or business dimension. Recently such tool like analytics was used mostly by huge and profitable companies, that can pay, including payments for data analytics. Now it’s time for common analytics usage. Such popularization is inspired by increasing understanding of analytics value and profits, which we get from making decision after analyzing. Setting hopes on Business Intelligencea major part of organizations already use it needs to focus on improving and optimizing benefits from decision making results. Other companies even don’t have clear analytical strategy and, on this basis, they need to create correct and effective one. Also, you should understand, that preparations and implementations depend on chosen model and it could take 3  ̶  7 months.  

In addition to external tasks solving, like vector of development, designation of decision effectiveness etc., also analytical research assists to solve internal tasks of the company, which is connected with employees’ motivation, resources and time. Statistically, the major part (59%) of companies uses analytics and monetizes in different ways. 

It’s already understandable, that analytics provides a lot of possibilities. In such case, one of the most important skills is a data sorting skill. At first, it’s critical to understand important and relevant components for every particular business. Among numerous questions and tasks can be solved with data analytics are advertising campaign optimization, revenue and spends analysis etc. And the main challenge is clear conception what goals we want to achieve and what kind of tasks will be solved in every situation. 

The COVID-19 situation became a perfect demonstration of data analytics values and benefits. Butch Works and the International Institute for Analytics conducted a survey, where were interviewed 300 analytics professionals around the US. Almost the half of them (43%) confirmed analytics major part in making important decisions for future business existence. 

Aaron Kalb (a Chief Data and Analytical Officer and Co-founder of Alation) mentioned that consequences and losses subject to pandemic will be increased. Moreover, as COVID-19 crushed and turned over each country’s economy particularly and the world economy in common, companies had to make unplanned investments in BI for making solutions and understanding how to work after.  

During last ten years the world has achieved the new figure in terms of data. Every organizations’ work, way of development, business strategy or choice depend on Data Analysis, which can transform it in different ways and change the vector. Meanwhile you always have a possibility to get all necessary data in short order, just in minutes. 

Why Intelligent Automation is a necessity

In the last few years, concepts like “Digital Transformation” have become so vague and confusing that it leads to businesses not knowing where to start, which results in disappointment and failure. The truth is, however, that a full Digital Transformation would require more than one technology; hence the term Intelligent Automation, which is the automation of the company’s processes, assisted by analytics and decisions made by Artificial Intelligence. 

Intelligent automation (IA) is already changing the way business is done in almost every sector of the economy. IA systems process vast amounts of information and can automate entire workflows, learning and adapting as they go. Applications range from the conventional to the groundbreaking: from collecting, analyzing, and making decisions about textual information to guiding autonomous vehicles and state-of-the-art robots. 

Deloitte and other independent analysts urge companies to include intelligent automation in their work processes otherwise they will be left behind. But what is IA, how are other businesses applying it, and how might it be beneficial for your business?

What is Intelligent Automation?

In brief, it’s the integration of two technological concepts that have been around for quite a while: artificial intelligence and automation.

Artificial intelligence encompasses things like machine learning, language recognition, vision, etc., while automation has become part of our life since the industrial revolution. Just as automation has progressed, so artificial intelligence has evolved, and by merging the two, automation achieves the advantages bestowed by intelligence.

You may have heard about robotic process automation (RPA). It’s a software capable of automating simple, rule-based tasks previously performed by humans. RPA can mimic the interactions of a person and connect to several systems without changing them as it operates on the graphical user interface or GUI. One disadvantage of RPA is that it needs structured data as input and can perform only standardized processes.

Intelligent automation gives software robots a method for learning how to interact with unstructured data. IA usually includes the following capabilities: image recognition, natural language processing, cognitive reasoning, and conversational AI.

Applications of Intelligent Automation 

IA is applicable in a wide variety of processes:

IA enables machines to collect and analyze situational or textual data and come up with an appropriate course of action.

IA helps its users deal with certain issues regarding the functioning of their businesses such as processing vast amounts of data or the problem of high labor costs and labor scarcity, among others.

With IA machines can scan the data, check it for accuracy, discover inconsistencies, and suggest multiple courses of actions suitable for a particular business requirement.

Advantages of IA for Decision-Making

Now let’s look at how IA improves decision-making across various industries.

Financial Services: Major investment managers use software robots to study research notes for consistency. Credit Suisse Group, for instance, analyses companies using a huge volume of data sources. The intelligent automation system they use can even write reports and arrive at conclusions without human intervention. The company says that its intelligent software has allowed it to improve both the volume of its research output and the quality of the reports it produces.

Prescribing Treatment Plans: IBM’s Watson helps medics to stay ahead of the curve. With a continuous stream of new developments and researches to process, doctors could easily spend many hours investigating the best treatment options for a patient only to miss some vital scrap of information. Cognitive computing technology allows Watson to propose treatment plans based on all the available evidence. 

Identifying Threats: Crime and terrorism have always been major concerns in today’s big cities. Humans can’t monitor security cameras 24/7. There are simply too many of them. That’s why cities like London, for example, implement systems that alert security analysts to possible threats after analyzing data from sensors and cameras.

Evaluating Creditworthiness: Quarterly financials are a good way of evaluating a company’s creditworthiness, but in a fast-paced business environment, significant changes in financial standing can fall between reporting dates. Intelligent software can monitor thousands of data sources, evaluating the information, and identifying risks that would otherwise have gone unnoticed. Furthermore, it offers more favorable terms in response to opportunities presented by companies with a positive credit outlook.

Workflow Software and Conditional Logic: On the surface, managing workflows through an automated system should be simple enough. But there are times when the outcome of a workflow, and the route it follows, depends on conditional logic. This could be more complex than a simple “if A=B then C” equation. Intelligent automation can evaluate a current situation based on all the factors and systems that impact on it, deciding on the best course of action to follow.

Physical Tasks and Intelligent Automation

We already understand basic automation in which “robots” carry out tedious tasks in production line settings, but machine intelligence has taken this to the next level allowing us to automate tasks that we could only perform manually in the past.

Distributing Products: Crate & Barrel and Walgreens are among the retail giants that are using robots that can improve the efficiency with which they fulfill orders. Robots travel around warehouses without colliding with other traffic. They fetch units loaded with products that will be dispatched and bring them to the teams responsible for order fulfillment and shipping.

Collaboration of Robots and People: Using robots in auto assembly is nothing new, but only a decade ago, robots and people worked separately for safety reasons. Then Volkswagen introduced a collaborative robot that works with human operators, taking over an arduous task that’s a part of an assembly process. If the human technician is in the way of the robot, it will react to the situation. It, therefore, needs no protective housing and can collaborate with its human “co-workers.”

Robot Soldiers: Intelligent automation is already being used in airborne drone technology, and there are even four-legged robots that can run, climb, navigate tough terrain, and respond to orders from a human commander.

Driverless Cars: Autonomous cars that you can send to do your shopping, collect a friend or family member, or simply use to get around safely, are a hot topic right now. Many believe that this advance will revolutionize the future of transportation.

Hauling Ore: Driverless trucks are already at work in Australian mines, and big mining companies see these autonomous vehicles as a way of improving productivity and worker safety. The trucks can navigate the site with little human intervention, and the company says it is saving up to 500 hours a year through its use of IA.

Key Success Factors for Achieving Intelligent Automation

Now that we understand the definition of IA and its benefits, we are faced with the usual problem: “How do I start to apply this to my business?”.

Here are 7 steps you should consider that will help you successfully implement intelligent automation.

1. Decide what success looks like

Knowing that intelligent automation will improve your business is one thing but making sure that you get backing and buy-in to roll it out throughout your company is another. Be clear on what goals you want to achieve; it will be easier to measure performance, manage the team, and celebrate success.

Your success can be measured in a clear metric like “a 20% reduction in operating cost” or a “70% improvement in throughput”, or it may be a less defined point similar to the ideas presented above. Whatever “good” looks like should be something you and others agree internally.

2. Identify IA candidates

Some automation initiatives are driven by a desire to improve a specific process or activity, but for most building an automation roadmap helps to prioritize where to start with automation.

The ideal candidates for automation may vary depending on the product or platform you choose. The following list will give you a few ideas on where to start identifying automation candidates:

Could you easily give a set of task instructions to a new employee? If processes can be defined and communicated to new workers, they are typically good automation candidates.

Is there a workflow guide or runbook? An existing runbook or workflow is not compulsory but it helps speed the process of building automation.

Does execution require the use of multiple systems and/or applications? Processes that involve humans as the interconnection between systems make for good automation candidates.

Is there room for ambiguity or feeling in the process? Processes requiring human judgment are not typically good candidates for hands-off automation. Although they may be suitable for assisted automation.

Is there a high-volume activity that isn’t overly complex? Tasks like these are a good way to quickly bring a return on your investment.

Is there an amount of work that requires human judgment to initiate, approve, or define? Processes do not need to be 100% automated to deliver benefit and the Digital Worker can be configured to do the bulk of the work while keeping the human in the loop for initiation, approval, or authorization.

3. Start small and scale fast

Intelligent automation is not the same as other digital transformation options. Its ability to digitally transform a business in a vastly reduced time is unmatched. The non-invasive nature of RPA in combination with AI and other intelligent technologies means it can be put into action within months. Many organizations are now running proof of value projects while deploying one or two in-action processes. Once these small-scale processes have proven their value, the automation journey can pick up full steam and scale across the business. You can either develop similar processes in the same vein or apply intelligent technologies in other ways. 

4. Secure executive sponsorship

When seeking an Executive Sponsor, it’s important to lay out the expectations of the role and its significance for the success of the project. Here’s what your Executive Sponsor needs to do:

5. Build the right team

There are a few critical roles in an automation team – and while a person may take on multiple responsibilities early in the program, as the team expands, they may become full-time roles or teams in their own right.

Head of Robotic Automation

Any digital transformation needs a leader with vision. The head of the team should see what part of the organization will benefit from automation. They are also responsible for buy-in at every level and in as many departments as possible, and for timely and successful delivery.


The architect is responsible for defining and implementing the optimal approach to automation. This team member usually uses models such as the Robotic Operating Model and creates capabilities to maximize benefits, scalability, and replication.

Process Analyst

The process analyst must capture and break down the requirements for a scalable and robust automation deployment. Documented and well-defined tasks can effectively be re-used if necessary, in part or in whole.

Automation Developer

The developer is responsible for building and delivering the process objects, in line with the best practice standards outlined by the vendor or other leadership team members. Depending on the automation solution you choose, this person doesn’t need to have coding expertise.

Process Controller 

Working closely with developers and analysts, the process controller runs the automation project on a day-to-day basis. From testing to the release, the controller runs and co-ordinates processes, flagging up any issues in the production and finding potential areas of improvement.

Technical Architect 

The technical architect is a key expert in a solution deployment process. Together with lead developers and other technical leads, the architect has the potential to raise awareness and explain how the digital workforce can work in an organization.

These are typical roles and responsibilities. Each will require a different level of understanding and skills with the automation tool, so you need to implement a training program that will ensure role-based education, preferably with certification or accreditation of skills to validate capability.

6. Communication is key

It’s an indisputable fact that Intelligent Automation will affect the way an organization operates. This may have a certain impact on staff, meaning that people might become uncertain or fearful. It’s important to address these concerns, and with full buy-in from leadership, explain in-depth the significance of the automation process. 

7. Build a Centre of Excellence (CoE)

CoE is an organizational team that sets out and drives the automation strategy that aligns with the business objectives. Other responsibilities of CoE include: 

When approaching the creation of a CoE, it is worth considering whether you want to take the centralized approach or the federated approach to automation. Our research shows that it depends on the situation. Think about how much control you needed over the deployment of automation and if allowing smaller teams to manage their own niche digital workers fits with your strategy.


Advances in artificial intelligence, robotics, and automation, supported by substantial investments, are fueling a new era of intelligent automation, which is likely to become an important driver of organizational performance in the years to come. Companies in all sectors need to understand and adopt intelligent automation, or risk falling behind.

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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