data

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.

What is the Year 2038 problem and how to fix it?

18 years from now, when the clock strikes 14 minutes and seven seconds past three on the morning of Tuesday 19 January 2038 UTC, a bug known as the Year 2038 Problem is expected to occur. Any computer, program, server or embedded system that store time using 32-bit signed integer will go haywire unless they are upgraded in advance. Some software that works with future dates has already begun to fail because it should have been patched even sooner.

Almost all operating systems in use today can be traced back to UNIX. When engineers developed the first UNIX computer operating system in the 1970s, they arbitrarily decided that time would be represented as a signed 32-bit integer and be measured as the number of seconds since 12:00:00 a.m. on January 1, 1970. 32-bit date and time systems can only count to 2,147,483,647 which translates into January 19, 2038 (3:14:08 am). On this date, any C programs that use the standard 32-bit time_t library will have trouble calculating the date.

The issue with signed integers is that they don’t behave like an automobile’s odometer. When a 5-digit odometer reaches 99 999 miles, and the driver goes one extra mile, the digits “turn over” to 00000. But when a signed integer reaches its maximum value and then gets augmented, it goes back to its lowest possible negative value. Adding 1 more to the maximum value of 2,147,483,647 will cause the integer to wrap around to its minimum value of -2,147,483,647 which represents December 13, 1901, at 8:45:52 PM GMT. Any affected computer will think it traveled back in time. This is called an ‘integer overflow’, and it means the counter has run out of usable bits and begins reporting a negative number.

Most of the support functions that use the time_t data type cannot handle negative time_t values at all. They fail and return an error code, and this results in the calling program crashing spectacularly. In particular, the bug affects the Unix operating system, which powers Android and Apple phones and most internet servers. Some programs that work with future dates may also start experiencing problems sooner. For example, a program that deals with dates 20 years ahead should have been fixed by 2018.

For Y2038 planning, an incremental and proactive approach is needed at this stage. Right now, some areas to focus on include: 1) software dealing with future times and dates; 2) on-the-wire message and file formats; 3) devices with long deployed lifetimes and their dependencies.

The most important area to focus on initially is software that deals with future dates, such as for handling X.509 certificates (like the ones used for HTTPS) and certificate authorities (CAs) or for financial planning. In many of these cases, it has been possible to resolve the issues by moving legacy software from a 32-bit integer time_t to a 64-bit time_t. In other cases, more extensive changes are needed, especially when times get cast into integers for math, when message wire formats get involved or for when values are stored in databases. In testing and fixing support for the 20-year CAs, downstream dependencies can come into play. If a date 30 years in the future gets fed into a logging system or monitoring system, and if those in-turns feed into alerting systems or reporting databases or provisioning systems, then those may also all need fixes.

The impact can extend well beyond a specific system when 32-bit timestamps are put into messages, databases, or file formats. These are also systems with external dependencies where more advanced planning is often needed as interactions across system boundaries. For these collections of interoperating systems, changes may need to be released in a specific order, and most of the time, backward compatibility comes into play. Furthermore, if there are either formally or informally standardized protocols that use 32-bit epoch timestamp values in messages, any migration or fix could be predicated on fixing the standard. As such, these become important to worry about as with a dependency chain such as:

If each of these takes a few years and the shipping product has a long lifespan, then the long lead-times here may already be a problem.

Devices with long deployment lifetimes should also be an area of focus. Embedded devices shipping with 32-bit hardware may also not have an easy fix of compiling for a 64-bit time_t via a software update. Connected automobiles, as well as other IoT devices, are likely to be an area of specific concern. Given current trends, it is likely that over 10% of cars sold today will still operate in Y2038, and with increases in vehicle age and some vehicles on the road, this may be even higher. We may end up with a significant fraction of automobiles with the potential to have serious issues in eighteen years. This same pattern exists in other embedded systems such as home gaming consoles and smart televisions where devices may ship with 20+ year CA certificates pre-installed.

Communications devices, such as cell phones and Internet appliances (routers, wireless access points) are another major use of embedded systems. They rely on storing an accurate time and date and are increasingly based on UNIX-like operating systems. People reported that due to the Y2038 problem, some devices running 32-bit Android crash and not restart when the time is changed to January 19, 2038.

Devices with long deployed lifetimes may require more comprehensive testing that the operating system and software continue to work properly before, during, and after the Y2038 transition point.

Like the Y2K bug, it’s a well-known issue, however, many people don’t consider it a serious threat. A common excuse you can find on forums and message boards is that by the time 2038 rolls around, there won’t be any 32-bit software or system left. But the Y2K fiasco showed that everyone underestimated the longevity of software architecture and how embedded that would be.

People tend to be short-sighted, thinking of now more than even the near future. Programmers thought the year 2000 was so far off, computers and software would surely be different by then! They didn’t need to worry about it—until the 1990s when the Y2K bug went from a non-problem to a mild panic, with the direst warnings talking of civilization collapse.

The total cost to fix the Y2K bug was over $300 billion, plus a few more billions spent on dealing with issues that appeared after the turn of the century. When the year 2000 rolled around—nothing catastrophic happened. None of the dire warnings of the Y2K bug manifested. This led many to believe that the whole thing had been blown out of proportions.

But there was no Y2K crisis thanks to all the programmers who put the effort to fix the problem, to change millions of lines of code so that 8 digits instead of 6 would represent the date. The irony is if you do your job properly, either no one notices, or they may even question the need for your job in the first place.

The lack of impact of Y2K may cause organizations and technologists to under-prepare for Y2038. It is harder to explain the “Y2038 problem” to laypeople than Y2K, potentially making it harder to prioritize and focus on advanced work. Numerous embedded Internet of Things (IoT) devices becoming ubiquitous also makes the potential impact considerably higher for Y2038 than it was for Y2K.

The solution isn’t technically difficult. We just need to switch to 64 bits or higher bit values, which will give a higher maximum. Over the last decade, a lot of personal computers have made this shift, especially companies that have already needed to project time past 2038, like banks that must deal with 30-year mortgages.

Apple claims that the iPhone 5S is the first 64-bit smartphone. However, the 2038 problem applies to both hardware and software, so even if the 5S uses 64 bits, an alarm clock app could still be 32 bits and so must be updated as well.

The problem does not seem too urgent — we have 18 years to fix it! — but its scope is massive. To give you an idea of how slowly corporations can implement software updates, a majority of ATM cash machines were still running Windows XP, and thus vulnerable to hackers, until April 2019 even though Microsoft discontinued the product in 2007.

So, it’s important to upgrade your systems NOW and be aware of the vendors that refuse to do so in time to avoid costly and short-term patches to your system and software.

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.

How to turn customer data into an asset

Nowadays, companies gather oceans of personal data and payment information from customers. According to numerous researches, the global amount of data will keep rising, thus prompting businesses to reconsider the way they handle the customer data. As an entrepreneur, you ought to contemplate whether data flowing through your organization is an asset or a liability.

Inaccurate data can be bothersome and cause a lot of problems. It won’t accurately show in which direction your organization is going, and it’ll impede business operations and processes, which in a post-GDPR world leads to fines. Furthermore, unreliable customer data will undermine your relationships with clients, business partners, and suppliers.

Once you contemplate the danger of data breaches and the ensuing penalties, you realize the existence of your company is tied to the way you deal with customer data. Therefore, you need to see it as an asset instead of a liability, and here’s how to accomplish this.

Pinpoint and tackle data challenges

Data comes from a multitude of resources, so keep in mind some data may jeopardize your system and cause a breach. To avoid this, it is important to standardize and cleanse all data sources frequently. Additionally, one needs to foster a culture of accountability and ownership within their employees so that everyone feels a personal responsibility for the data passing through their system.

Develop a Data-Oriented culture

Fostering a culture that will ensure data quality is desirable, too. If your company collects personal and payment information from the clients, establishing a data managing strategy is a must. But for it to work, you need to synchronize your processes and systems first.

As you grow your business, the most advisable course of action is to instill the importance of cybersecurity and data privacy in your employees. Encouraging a data-oriented culture will result in everyone treating each data set as the asset it is. The payoff of promoting such culture will be invaluable to your company, and you’ll realize this when it’s time to reap all the rewards.

You’ll also be able to gain insights into the market, but to do it effectively, consider implementing an automated policy that will ensure the consistency of your data. What’s more, an automated solution can standardize data quality across the whole organization.

Do the cleanup

A substantial amount of customer data you collect is insignificant. Keeping all the data will only weigh you down as you’ll be struggling to reach set objectives. It’s not sensible to spend resources on storing and securing unnecessary and disorderly data, which is why you should get rid of it. If unsure of the validity of your data, conduct an audit to determine what you have and whether it’s important or not.

Use data to plan ahead

Since accurate customer data sheds light on your company’s performance, it should become an integral part of your operational setup. Utilize it when making decisions and plans, integrating strategies, and building partnerships. A data strategy, created with all your organization’s needs in mind, will propel you towards your business objectives.

Make the most out of data

To benefit from the data and turn it into an asset, you need to exercise control over it and develop a data-focused strategy that could help you derive value from every data set you have.

Turning customer data into asset calls for innovative data analytics, AI, machine learning, and big data solutions. These will help you understand data better and, as a result, gain insights faster. You’ll start noticing more exciting business opportunities, and if the data is accurate, you’ll also improve your marketing, sales, and customer service.

Customer data doesn’t have to be a liability because you’ve got all the power to transform it into an asset for your business. It requires time and patience, but the pay-off will be worth it.

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