data analytics

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.

Architect

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.

Conclusion

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.

Data Quality and Master Data Management: A brief guide to improving data quality

In the modern data-driven world, the importance of data quality and master data management (MDM) is indisputable. In its pure, chaotic form data is useless, but if it’s of high quality, it can become a tremendous advantage for business leaders. Unfortunately, as the company collects more and more data, the risk of data becoming ‘dirty’ increases. Around 27% of business leaders can’t vouch for the accuracy of their data. Dirty data is the product of human error, duplicate data, the passage of time and other factors. It can undermine the efficiency of analytics and machine learning and cost the company 12% of its revenue.

According to The BI Survey, data quality is one of the biggest problems for BI users since 2002. In this article, we’ll explain what Data Quality and Master Data Management (MDM) is and how to improve it.

Defining Data Quality and Master Data Management

There is no single definition of data quality. Rather, data quality is considered good if it can be used for a certain purpose. It also has a few characteristics. Good quality data is consistent, up-to-date, accurate, complete, valid, and precise. However, a set of data can be good in one context and useless in the other. Knowing how many items the store has sold may be enough to place an order for the next month, but this data doesn’t show whether there was a profit.

This is why we need Master Data Management (MDM). It helps collect data from different sources and coalesce it into a substantive whole. Among other situations, MDM comes in handy when:

…aside from an ERP system, your company works with other SCM or CRM systems and needs consistency across these platforms

…you need to ensure effective cooperation with business partners and fabulous customer experience

…your company needs to merge on-premise and cloud-based systems

Many respondents to the BARC Trend Monitor surveys consider data quality and MDM as one of the most important trends. BI specialists hold the same opinion because they know the popular self-service BI technologies and data discovery tools are valuable only when they’re fed good-quality data.

Steps to improve Data Quality

To enhance data quality and MDM, you must adopt a holistic approach that would address your company’s modus operandi, data quality assurance processes, and technologies. The company ought to define clear responsibilities for data domains (e.g., customer, product, financial figures) and roles. Establishing processes to assure data quality will be easier if you adopt great practices like the Data Quality Cycle. Apt technology is important too, but it’s crucial to focus on the organization and its processes first since they are pertinent to your company’s strategy.

Now let’s look at some concrete steps to improve Data Quality.

1. Assign clear-cut roles

You cannot improve data quality without fostering a culture within your company that recognizes the significance of data for generating insights. This culture includes defining clear roles that will ensure the data is gathered and treated responsibly. Roles help with assigning tasks to certain employees based on their capabilities. The typical roles are:

2. Adopt the Data Quality Cycle  

You cannot check data quality once and then forget about it. This is an ongoing project. That’s why it’s best to do it using an iterative cycle of analyzing, cleansing and monitoring of data. You can break down the cycle into the following phases:

Data Quality goals are defined according to your company’s needs. It will give you a clear understanding of what data you should focus on. To outline these goals, you can start by answering questions like “How can we define the data domain?” or “How can we identify that data is complete?”

After establishing the metrics, you need to use them to analyze data. Here some essential questions are “Is the data valid?”, “Is the data accurate?”, and “How can we measure data values?”

To reach the data quality goals, you need to clean and standardize your data. There is no universal rule on how to do it because every organization has its own standards and regulations.

You can enrich your data using other data such as socio-demographic or geographic information. This way, you’ll develop a comprehensive and more valuable dataset.

As we mentioned earlier, it’s crucial to constantly check and monitor your data since it can quickly become irrelevant or erroneous. Thankfully, there is software that allows you to automatically monitor data according to the pre-defined rules.

3. Use the Right Tools

Most technologies support Data Quality Cycle and offer extensive functionality to assist different user roles. To use such technology to the fullest, you need to integrate the phases of the data quality cycle into the operational processes and match them with a specific role. Carefully chosen software can aid in:

These are just a few examples of modern data management tools’ functions. The full list is quite impressive and should encourage you to prioritize the functions relevant to your business needs.

Better late than never

The complexity of the issue may be intimidating but in the era of digitization, maintaining a high quality of data is a must. Accurate and reliable data can guarantee excellent customer service, intelligent business decisions, and economic prosperity for your company. Like all good things, it requires some effort, but, ultimately, data quality management will pay off.

Qlik becomes a part of Snowflake Partner Connect Program

This week Qlik partnered with Snowflake, a cloud data warehouse. The partnership involves Qlik’s integration with the Snowflake Partner Connect program which will provide Snowflake customers with a two-week free trial to fully experience Qlik’s first-class data integration software. The free trial comprises tutorials for swiftly ingesting and delivering data in real-time to Snowflake. Extension of the trial enables users to export data from numerous popular enterprise database systems, mainframes and SAP applications. With Qlik Data Integration platform, it’s also possible to automate the creation and updates of analytics-ready data sets in Snowflake.

“Our customers want to accelerate their modernization efforts by utilizing highly performant and robust solutions to replicate data into Snowflake,” stated Colleen Kapase, Snowflake VP of WW Partners and Alliances. “With Qlik’s real-time data integration capabilities, customers will realize an immediate benefit to easily bringing that data directly into Snowflake. We are excited about Qlik joining our partner connect program, bringing new capabilities for customers to modernize to Snowflake.”

Snowflake Partner Connect empowers new users to effortlessly connect with and integrate specific Snowflake business partners straight into their experience when creating trial accounts. With Qlik Data Integration, customers can access a wide selection of enterprise data sources in real-time and gain the most value during a Snowflake evaluation. After completion of the trial, there is an easy way to purchase the full license of Qlik Data Integration.

“Snowflake gives us a scalable data lake environment, bringing data together in one location from any source. This enhances decision making across all our varied business functions, including manufacturing, supply chain, customer service, and financing,” affirmed Dallas Thornton, Director of Digital Services at PACCAR. “Qlik’s data integration software is a huge driver in the value we see with Snowflake. Since it streams disparate data sources using change data capture into Snowflake from any platform – be it cloud, x86 databases, mainframes, or AS400 – our users now have one environment in Snowflake from which to analyze data in near real-time.”

“We’re excited to expand our partnership with Snowflake by joining their partner connect program, helping enterprises accelerate their journey to cloud data warehousing,” proclaimed Itamar Ankorion, SVP Technology Alliances at Qlik. “Qlik has a complete solution for Snowflake that continuously ingests all targeted data, automates the warehouse/mart creation without scripting, and makes data and insights readily accessible across the organization with world-class analytics.”

About Qlik

Qlik’s vision is a data-literate world, one where everyone can use data to improve decision-making and solve their most challenging problems. Only Qlik offers end-to-end, real-time data integration and analytics solutions that help organizations access and transform all their data into value. Qlik helps companies lead with data to see more deeply into customer behavior, reinvent business processes, discover new revenue streams, and balance risk and reward. Qlik does business in more than 100 countries and serves over 50,000 customers around the world.

Qlik became a Gartner Magic Quadrant leader for the 10th year in a row!

Yesterday Qlik announced they had been named a Gartner’s Magic Quadrant Leader for Analytics and BI Platforms for 10th year in a row. This recognition marks not only a decade of Qlik’s continuous leadership in the quadrant but also inclusion in Gartner’s MQ since 2006.

“Qlik is helping customers accelerate business value through data, providing a full range of capabilities to go from raw data to real-time insights and action,” said Mike Capone, CEO of Qlik. “Our company continues to grow profitably, and our strong performance has enabled us to invest in delivering an end-to-end platform that includes data integration, AI-driven insights, and conversational analytics. With our recent acquisition of RoxAI we are providing automated intelligent alerting for real-time decision making as we continue to invest in capabilities that increase data’s value for every organization.”

The report, which Gartner releases annually in February, provides an unbiased evaluation of analytics and business intelligence (ABI) platforms, analyses the market and highlights its biggest trends. This time, an appraisal of the platforms is no longer based on their data visualization capabilities since those are becoming mainstream. Instead, the focus is shifting towards integrated support for enterprise reporting capabilities and augmented analytics.

“Machine learning (ML) and artificial intelligence (AI)-assisted data preparation, insight generation, and insight explanation — to augment how business people and analysts explore and analyze data — are fast becoming key sources of competitive differentiation, and therefore core investments, for vendors.” – Gartner, 2020

Gartner lists the following strengths of Qlik:

“Empowering employees with the right information and the confidence to make decisions with it is vital,” said Director of Business Intelligence, Visualization and Reporting at Nationwide Building Society. “Qlik has proven to be fantastic in helping to consolidate disparate data, break down internal silos and drive value. By making data more visible and intuitive, business teams have gained new insights across many processes, increasing efficiency and fostering a data-enabled culture.”

You can download a copy of the full report here.

Qlik's Statement of Direction 2020: Exciting developments from Qlik that are waiting for us in 2020 and beyond

As we’re stepping into a new decade, Qlik releases their Statement of Direction which provides an exciting overview of Qlik’s product direction and forthcoming offers. We encapsulated this fascinating read into 15 sentences that should hype you up for using the Qlik Analytics Platform in 2020 and beyond.

Qlik Sense

  1. New capabilities for customers to add business logic to the Qlik Cognitive Engine, and new sources for machine learning – including governed libraries, the analytics ecosystem and external, domain specific sources.
  2. New types of augmented analysis which include key driver analysis, statistical, predictive and prescriptive insights as well a new extension for multi-attribute (cohort) analysis.
  3. Improvements for advanced analytics integration performance, augmented data stories and content recommendations.
  4. New visualization, analytics and authoring capabilities that include moving averages, difference functions, time-based forecasting, trend indicators in tables and sparklines.
  5. Introduction of grid and bullet charts.
  6. Dynamic views, a new capability enabling in-memory database views for products such as Snowflake, SAP HANA and more.
  7. Check out / check in functionality for app objects supporting team-based development.
  8. New self-service reporting that will support authoring, scheduling and personalized distribution.
  9. A new user experience for insight management that will allow people to capture, organize, share and take action on the most relevant insights – including charts, AI-generated insights, snapshots, reports, stories and more.
  10. Annotations and discussion threads, content following and social BI in a multi-cloud hub, an insight library with tasks, goals and approvals, and workflow automation through the platform.

Qlik Sense Mobile

  1. Automatic downloads of updated offline apps and support for offline mashups.

Qlik Insight Bot

  1. Integration with the Qlik Cognitive Engine, allowing for enhanced natural language capabilities surfaced in visual and conversational user experiences.

Qlik NPrinting

  1. Integration of report distribution capabilities directly into Qlik Sense.

QlikView

  1. Common scheduling with Qlik Sense

Qlik Connectors

  1. Configurable REST connectivity with Azure Data Lake, updates to Essbase connectivity, integrated connectivity to new data sources such as Amazon Athena, and expanded support for SAP HANA.

If you’d like to read Qlik’s Statement of Direction 2020, you can find it here.

Summary

The Statement of Direction 2020 suggests that from now on Qlik will be focusing on integration. They’ve acquired many great additions such as RoxAI and their Ping solution to build up a multidimensional platform, but unfortunately it lacks integration. That’s why it’s so amazing to see Qlik working on this issue while also advancing the field of analytics and expanding their platform with new additions that soon will combine into a powerful unit.

Data and Analytics trends that will transform business landscape in 2020 and beyond

Modern businesses must deal with colossal amount of data which can be overwhelming. On the flip side, being able to obtain insights from the massive pool of data is beneficial since it helps to make well-informed decisions that propel growth. Brand new BI, data and analytics technologies emerge all the time and it’s important to recognize and embrace those that will help your business gain a competitive edge.

But don’t wait until new technologies grow and mature! Don’t be afraid to engage with them and explore their capabilities. Through trial and error, you’ll be able to find a solution that suits the needs of your company best. At the same time, BI and analytics service providers ought to adopt new technologies to provide their clients with competitive advantage.

We present you the list of data and analytics trends that will shape the business landscape in 2020 and beyond.

Augmented Analytics

Coined by Gartner in 2017, the term Augmented analytics refers to the use of AI, machine learning and natural language processing to enhance data preparation, data analytics and business intelligence.

To glean insights from data, one needs to collect and analyze it. These tasks are the responsibility of data scientists who spend approximately 80% of their time only on data preparation. The remaining 20% is spent on putting this data to good use. With augmented analytics, the initial stages of this procedure can be automated. What’s more, the goal is to get rid of data scientist altogether and even entrust search for insights to AI. Although this should speed up the process of making business decisions, it requires adequate data literacy among employees.

According to Gartner report, augmented analytics are expected to influence the increase in purchasing ML, data science and BI solutions.

Augmented Data Management

Data is collected from various resources so It’s not surprising data scientists spend a lot of time refining it. Augmented Data Management (ADM) allows businesses to cleanse data automatically using artificial intelligence and machine learning. Thus, organizations can eliminate unnecessary and tedious work of data scientists, speed up their productivity and ensure the quality of the data. What’s more, ADM can be useful for data engineers. It will notify them about potential errors and data issues and offer alternative interpretations of data.

ADM will likely cause a big splash during the following years. Gartner predicts that by the end of 2022 ADM will reduce manual tasks by 45%. Further reliance on AI and ML will reduce the need for data management specialists by 20% by 2023.

NLP and Conversational Analytics

Natural Language Processing (NLP) is a branch of AI that makes conversation between humans and machines possible. It’s a technology that allows computers understand written and spoken human language. The most prominent examples where NLP is used are Google, Grammarly, Interactive Voice Response, Siri, Cortana, Amazon Alexa, etc.

NLP grants businesses an ability to inquire into data and gain better understanding of generated reports. Conversational analytics is a technology based on NLP that can provide insight into how users interact with your chatbots or other AI-based interfaces in real time.

Data analytics tools can be demanding, but with NLP, even non-specialists will be able to request information from databases and other less structured sources of information with no effort.  According to Gartner, by 2021, companies will adopt BI and analytics tools for more than half of their employees comparing to 35% of employees that use such tools now. Among new types of users there will be a company’s front-office staff.

Graph analytics

An emerging and exciting form of data analysis, graph analytics works exceptionally well with visualizing complex relationship between data. It utilizes graph format to represent data points as nodes and relationship as edges. This format is the most suitable for finding indirect connections between data points or analyzing data based on the quality and strength of the relationship.

Graph analytics prove to be useful in various fields such as logistics, traffic route optimization, social network analysis, fraud detection, and more. As businesses continue to explore capabilities of big data, graph analytics will become a must-have for deriving a more complex and profound insights. Gartner predicts that in the forthcoming years application of graph analytics will grow at a rate of 100% annually.

Commercial Machine Learning and Artificial Intelligence

Nowadays AI and ML market is dominated by open-source platforms like Python, Apache Spark and R, but, according to Gartner, it’s about to change. Open-source platforms were supposed to democratize the market and make advanced technology available to everyone. Sure, most innovations pertaining to algorithms and development environment over the last five years have occurred on open-source platforms. But open source has some serious drawbacks when it comes to scalability of AI and ML.

At Gartner, they estimate that by 2022 75% of new ML and AI solutions will be based on commercial rather than open-source platforms. Commercial vendors, which at first were slow to adapt, are finally catching up by establishing connectors to open-source ecosystem. Furthermore, they’re introducing features necessary for scaling AI and ML on the enterprise level, e.g. project and model management, transparency, data lineage, platform integration etc. Thus, businesses can combine innovations of open-source platforms with enterprise-ready tools offered by commercial vendors and deploy models in production more efficiently.

BI tools as a pivotal asset to your business

In a modern data-driven world, businesses that use data to good advantage are at the top of their game. But for many, the amount of data can be too large to handle, mainly because they don’t have apt specialists or technologies such as BI tools. The latter are important because they allow you to collect unstructured data from various resources, analyze it, and derive insights from it. There are lots of BI solutions serving particular goals but the common benefits they offer are a reduction in cost and time spent on data management, an increase in revenue, ability to access data in real-time and more.

BI tools can boost performance in any part of your company, however, where they are needed the most is the financial department, the marketing department, and CRM.

Harnessing the power of financial data

The significance of BI tools for the department of finance can hardly be overstated as the opportunities they offer are immense.

BI tools can help your company discern internal and external factors that affect your market performance. BI dashboards will present the holistic view of your firm’s financial situation allowing you to detect and deal with problem areas in time. Analysis of historical data will reveal potential risks as well as future trends so that you can build more effective strategies.

Predictive analytics tools will be of great service when it comes to gaining insight into customers, products, and even employees. For example, you can target profitable customers more efficiently by studying their buying patterns and demographic information which BI tools can gather.

It will also be easier to foresee fluctuations in the popularity of a product and avoid overstocking or understocking.

Besides, you can use BI tools to retain employees. Losing employees is damaging both to the company image and performance, so by analyzing the behavior of the current employees and the ones who left, you can take preventive measures and address the issues faster.

Finally, nothing improves the ability to take actionable decisions better than a clear understanding of the company’s KPIs, which is another factor every executive needs to keep a close eye on. BI tools will provide you with the up-to-date report on the crucial financial figures such the Operating Cash Flow, Net Profit Margin, Burn Rate, etc.

The important thing to note, however, is that it’s imperative to standardize data in your organization before the implementation of a BI tool. Standardized data makes financial reporting speedier and more straightforward because data isn’t being pulled from multiple resources which may contain conflicting or simply wrong information.

Planning successful marketing strategies

Marketers heavily rely on data when planning campaigns, targeting potential clients and allocating resources. So, no matter how good a marketing strategy looks on paper, if it’s based on inaccurate data, it won’t get you anywhere. This is another field where BI tools can save the day by presenting data in a way that will make marketing strategies bulletproof.

As we’ve pointed out earlier, BI tools can help narrow down the target audience and reach the right people at the right time. But the abilities of analytics software go well beyond analyzing demographics, and to see the bigger picture, it’s necessary to study engagement, purchasing and interacting patterns, and ROI.

Social analytics tools will be useful for creating a comprehensive target audience profile because apart from analyzing customer behavior, you can also gain insight into their thought process.

Social analytic tools comprise two facets: web analytics and social media analytics. Web analytics will show who visits your website, where they come from and what draws their interest. These tools highlight the successful areas of the site as well as the links that generate traffic.

Social analytics tools collect information from posts, comments, likes, shares, etc. on Facebook, Twitter and other social media. Such data allows to understand consumers’ needs and wants, take note of the criticism and praise, and adjust the marketing campaign accordingly.

Lastly, marketers must keep track of KPIs as well to evaluate the success of the campaign. Metrics like Cost Per Lead acquisition reveal the cost of each lead and where the most expensive and valuable ones come from.

Improving customer relations

We’ve already outlined how BI tools can offer a glance into the mind of a customer and how easy nowadays it is to listen to what they have to say. Some would argue pleasing modern consumers seems harder than before, but with the apt tools and the right attitude you’ll find out it’s quite simple.

When a person contacts customer service, they expect to receive a personalized solution to whatever problem they have. Having collected necessary data on this client, you can already predict what may trouble them and how to address this problem. As a result, the person feels heard and their opinion appreciated. This becomes a pleasant and authentic experience for both sides.

To further empower your customers, implement a BI tool they can use to manage their costs and control what they pay for. This way they won’t be feeling like a mindless puppet knowing they also have a say in the matter.

Conclusion

The influx of data can open businesses to a lot of exciting possibilities once they learn how to use BI tools. Business intelligence can optimize performance across the whole organization and help you gain a sustainable competitive edge. It may be considered one of the most valuable business assets these days since it can improve not only the financial health of your company but also your marketing strategy and customer service.

The significance of big data for complience

In the modern world, where technical progress goes hand in hand with digitization, it’s no surprise that Big Data is on the rise. We produce 2,5 QB of data every day, and this number will skyrocket to an astonishing 163 ZB by 2025, according to an IDC report.

The majority of businesses in virtually every industry have to deal with big data and analyzing it is the hardest part. Considering frequent cyberattacks and recent data breach scandals, the public was outraged and deeply concerned for the privacy of their data, and so stricter regulations to ensure their safety started to appear. As a result, businesses that wish to benefit from the exciting prospects that big data opens must establish a way to analyze data appropriately and avoid breaches by detecting and closing loopholes in time.

What is big data?

The term is not so transparent as it may seem. It can both mean a large volume of structured and unstructured data and ways to analyze, mine and extract value from it. Traditionally, big data is characterized by the three V’s: volume, velocity, and variety.

Volume is the amount of data collected from multiple sources such as social media, real-time IoT sensors, customer databases, business transactions and more.

Variety is the types and formats of data which can be structured like in databases, unstructured (text, images, audio, video files) and semi-structured (web server logs and sensors data).

Velocity refers to the speed at which data is generated and must be processed to deal with business challenges and gain valuable insights. Things like IoT sensors and smart metering necessitate dealing with data in real-time.

Some organizations expand on the mainstream definition by adding another two Vs: veracity and variability. Veracity is the quality of gathered data which can vary greatly due to the sheer number of sources. Bad data can negatively affect analysis and compromise the value of business analytics.

Variability concerns inconsistencies in data, a multitude of data dimensions from numerous data types and sources and unpredictable data load speed.

The companies that deal with big data need to abide by the regulations of different compliance bodies. They must provide detailed reports on the type of data they obtain, how they use it, whether they make it available to vendors, and the employed security measures to avoid data breaches and leaks.

As we mentioned before, it’s not easy to analyze big data. The process calls for highly sophisticated analytical tools and qualified specialists that would guarantee the fulfillment of compliance requirements. Although it sounds overwhelming, the enormous benefits are worth the trouble.

The connection between big data and compliance

Big data impacts the compliance process since companies must keep track of its flow in their systems. Regulatory agencies pay close attention to every stage of data handling, including collection, processing, and storage. The reason for such strict control is to make sure that the company keeps its data out of reach of cybercriminals.

To get the compliance status, the company needs to develop solid risk mitigation strategies. When analyzing data, you’re expected to demonstrate how each of these strategies work and their efficacy. Penetration tests must also become a necessary procedure to protect the company’s infrastructure and data. It involves simulating a malware attack against a system to detect any vulnerabilities. A thorough report on the data security system will help the company to become certified faster.

Unlike the organizations that rely on small data, handling big data during the compliance process is costly, since the company must use sophisticated analysis tools and employ qualified experts. But it’s necessary in order to harness big data power to predict cyberattacks.

The benefits of big data for the compliance process

One of the biggest advantages of big data is its ability to detect fraudulent behavior before it reflects badly on your organization. CSO online report states 84% of organizations use big data to detect cyber threats and report a decline in security breaches. However, 59% noted their agency was still jeopardized at least once a month because of the overwhelming amount of data, lack of the right systems and specialists, and obsolete data.

We’ve already covered the importance of qualified staff and powerful tools, and these are not the most important factors. The most crucial one is the automation of tasks so that data can be sent to analysts without delay.  Using machine learning and AI to develop a predictive analysis model will also greatly fortify the company’s IT infrastructure since it both helps fend off known ransomware but also predicts the new. All this speeds up the compliance process and gains customers’ trust.

Big data also helps to manage the risk which arises from sharing the company data with a third party like vendors. Analyzing their ability to protect your data, you can decide whether to share it or not.

To get a compliance certification, the company must prove its customers are satisfied with the way their data is handled. Applying big data analytics will help understand the customers’ behavior. Based on these insights, the company can adjust its decision making, thus simplifying the compliance process.

If your organization wants to obtain and benefit from compliance certifications, you must adopt big data analytics and develop a preventive compliance strategy instead of the reactive one. It will allow to identify threats from a mile away and take appropriate security measures.

GoUp Chat