data

Benefits of using unstructured data

With corporate data growth the volume of unstructured data is growing in parallel. Their volume is increasing annually at a rate of 55 to 65%. By ignoring such data companies don’t receive certain knowledge and don’t have a possibility to use it for analytics. This automatically doesn’t allow them to use all the possibilities. However, it is very important to know how to properly use unstructured data to achieve business goals.

Unstructured data benefits:

  1. Product development. With the help of unstructured data it is possible to study users’ moods and needs, analyze requests that come to the support service or social networks. This approach will improve company service or product;
  2. Sales and marketing. In this case, unstructured data is used to identify shopping trends and brand perception. The advantage of such data is the ability to assess consumer sentiment. Studying social media posts, forum discussions, support calls, and more can help increase sales and marketing strategy effectiveness. Unstructured data usage by CRM algorithms allows to conduct predictive analytics and know in advance consumers’ desires. So, employees of the sales department will be able to offer the necessary product or service to the consumer in time;
  3. Customer service. Automated chatbots allow to direct customer requests to the right people to resolve the issue quickly. Then the analysis of these issues is carried out, as mentioned above. This allows not only to know consumers’ moods and wishes, but also to identify effective and inefficient features of a product or service. This, in turn, will allow to improve the product or service.

Using unstructured data for BI involves 3 main steps:

  1. Determine the purpose of using unstructured data. It is necessary to clearly understand what problems need to be closed with the help of external data and how exactly it will be used;
  2. Optimize data sources. To create a set of valid data it is necessary to create a common data model. Since unstructured data is drawn from different sources and in different formats, it is possible to ensure data consistency and reliability by quality data flows creating;
  3. Create a plan and upgrade data processing programs. It is worth partnering with providers of high performance and high-quality data integration applications and resources. The key issue is an internal interface and methods definition for connecting data sources.

Cyber Security: What is this and why it is important?

Today we have almost unlimited possibilities for working with data, including their processing, storage and exchange between users. However, this raises the issue of cybersecurity.

Cybersecurity is an important part of any organization workflow. Its main goal is to protect all data categories (confidential data, corporate data, personal information, medical information, intellectual property, government and industry information systems etc.) from leakage and damage. The absence of a corporate cybersecurity project leads to an inability to withstand data leakage.

The modern world, each of us and society is highly dependent on technology, and this dependence will continue. Every modern company regardless of size depends on computer systems. The constantly growing number of users, devices, programs, the increasing data flow including secret or confidential data is of interest to cybercriminals. Global connectivity and cloud services usage to store sensitive data and personal information, misconfiguration, and sophisticated cybercriminal methods increase the risk of cyberattacks.

Maintaining cybersecurity in an ever-changing threat landscape is a top priority for all organizations. The use of ready-made solutions (antivirus software, firewall) cannot provide 100% protection, which confirms the relevance of creating a corporate cybersecurity system.

What is cyber security?

Cybersecurity is the process of protecting Internet-connected computer systems, networks, devices, and programs from any type of cyberattack, and recovering from any type of cyberattack. This practice is used by both individuals and legal entities to protect against unauthorized access to data centers and other computerized systems. Cyberattacks now pose a greater threat to data as cybercriminals bypass traditional data protections using new methods of infiltrating the system based on the use of social psychology and artificial intelligence.

A cybersecurity strategy can provide strong protection against attacks that are aimed at gaining access, changing, deleting, destroying or extorting confidential data of companies, organizations or individual users. Also, cybersecurity is able to prevent attacks aimed at shutting down or disrupting of operation systems and devices.

Governments around the world pay great attention to cybercrime. An example is the GDPR, which required organizations operating in the EU to report data breaches, appoint a data protection officer, require user consent to process information, and anonymize data for privacy. In the US, data breach laws are in place in all 50 states. Basic requirements: notify the victims as soon as possible, notify the government, pay a fine.

Elements of cybersecurity

The cybersecurity system can be divided into subsections, the internal coordination of which is critical to the entire cybersecurity system. Such subsections include:

Benefits of cybersecurity

Data Infrastructure and its key elements

Competent work with data brings business to a leading position. Incorporating data-driven innovation into operational business processes provides up-to-date information across all enterprise directions, leading to more efficient operations. During the 4th industrial revolution, data is the fuel for Artificial Intelligence, Machine Learning, robotics, the Internet of things, etc. According to forecasts, by 2025 the digital transformation wave will reach 3.7 trillion US dollars.

Data by itself has no value. It acquires value with the right working approach: having a data strategy, skills to work with it, a management process and an infrastructure that includes software and technical tools for collecting, storing, processing and transmitting data.

Although infrastructure is an important element of the data workflow, starting with infrastructure is not entirely correct. Sure, it will be necessary to invest into devices, applications, platforms and services that will provide efficient work with data. But the work begins with setting goals and developing a strategy, tailoring the tools to the strategy, challenges, and business issues.

With the increasing desire to capture data value and the growing demand for the technical means to enable it, the market for platform and solution providers has expanded significantly. This situation made it possible to reduce the entry barrier to work with advanced technologies and analytical solutions. Some of these offerings are called infrastructure as a service. Selecting the right product from a wide range of products requires doing a lot of research, understanding business needs, and identifying questions that need to be answered by introducing a new product.

Key functions that the infrastructure should provide:

  1. Data collection. Injecting internal (transactional data, customer feedback, cross-departmental data) and external data (data from social media, public sources, purchased third-party data) into the infrastructure stack. The process of collecting streaming data in real time must also be provided, which requires a reliable collection infrastructure.
  2. Data storage. Depending on data privacy level it’s possible to store it locally in own storage or in the cloud. Cloud storage providers provide free access to data for business users from anywhere. It also reduces the initial cost of setting up own servers, energy, and security.
  3. Data processing and analysis. At this stage work begins with machine learning, computer vision, speech processing, neural networks, etc. The main task here is to find a solution for preparing and cleaning data, building analytical models and extracting valuable information from unprocessed information.
  4. Obtaining information and disseminating it to business users. It is the stage of data visualization and reports creation with the help of which business users can make decisions, share information, improve internal processes efficiency, create improved products or services.

Data science development trends in 2022

The development of technologies such as deep learning, natural language processing, computer vision became possible with the emergence of data science as an area of ​​study and practical application. It also allowed machine learning (ML) to emerge.

Data science is a branch of computer science that studies various problems of data analyzing, processing and presenting in digital format. It covers the theoretical and practical applications of ideas, including big data, predictive analytics, and Artificial Intelligence. Up until 10 years ago, data science was considered a niche cross-sectional subject that combined statistics, math, and computing. Now, its availability is increasing, and its importance for business is understood. There are many ways to learn it, including online courses, in-house training, etc. Let’s consider some of the data science development trends in 2022 and beyond.

Small data and TinyML

Big data is often referred to as the growth in digital data that is generated, collected and analyzed by humans on a daily basis. Machine learning algorithms for processing large data amounts can also be quite large. Thus, GPT-3 is the largest and most complex system capable of simulating human language. It consists of about 175 billion parameters.

Machine learning can add value to cloud systems with unlimited bandwidth. That’s why the concept of «Small Data» arose and makes it possible to simplify the quick cognitive analysis of the most important data in situations where time, bandwidth, energy costs are essential. For example, self-driving cars can’t count on the ability to send and receive data from a centralized cloud server trying to avoid an accident.

TinyML refers to machine learning algorithms that take up as little space as possible and can run on low-power hardware near the scene of the action. In 2022, the number of its appearances in embedded systems (household appliances, cars, industrial equipment, agricultural equipment) will increase and make them smarter and more functional.

Data-driven customer service

Customer data is the main source of companies to improve the quality of customer service: product or service upgrading, the e-commerce process simplifying, a more user-friendly interface creating, waiting times reducing, etc.

The interaction between the client and the company is becoming more digital. Any action can be measured and analyzed for a better understanding of how processes can be improved, as well as personalized goods and services offered to the client. The pandemic has sparked a wave of investment and innovation in online commerce technology. Companies sought to completely replace physical shopping trips. Finding new methods and strategies to use data to improve customer service will remain one of the top trends in 2022.

Deepfake, generative AI, synthetic data

Deepfake is a realistic substitution of photo, video, audio content based on generative AI. This technology is widespread in the arts and entertainment. Deepfakes are expected to spread to other industries and use cases in 2022. For example, creating synthetic data for training machine learning algorithms. By creating synthetic faces of non-existent people in order to train face recognition algorithms. This will help to avoid problems with confidentiality and real people faces usage. Also, the application of this technology is possible in medicine (for example, for training systems for recognizing signs of rare cancer types); for converting a language into an image (for example, creating a building image based on a verbal description of its type).

Convergence

Digital transformation key elements are Artificial Intelligence (AI), Internet of Things (IoT), cloud computing, superfast networks (5G). Each of these technologies exists in isolation, but they are all interconnected, allowing to do more. For example, AI allows IoT devices to act intelligently, interact with other technologies with minimal human intervention. It contributes to automation and the creation of smart homes, factories and even cities. 5G and other superfast networks allow to transfer data at higher speeds. Moreover they will allow to become commonplace with new types of data transfer. AI algorithms play a key role in routing traffic to ensure optimal transfer rates, automating control of the cloud data center environment. In 2022, the development of these technologies and their interaction with each other will be observed.

AutoML

AutoML (Automated Machine Learning) helps to democratize data science. Data cleansкаing and preparing is a time-consuming routine for a data scientist. AutoML assumes the automation of such tasks. The goal of this technology is to create tools and platforms that anyone can use. Thus, with the help of user-friendly interfaces, each user can apply machine learning to solve problems and validate ideas. It is predicted that in 2022 AutoML will actively evolve to become an everyday reality.

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.

Storage is the key element of data-driven process

Data analytics is an indispensable modern business tool. Data understanding and its analytics provide comprehensive answers to the business on how to set up processes for maximum benefit, who is the main business customer and what he needs, what «gaps» exist in activities. In addition, companies use artificial intelligence to offer products and services to the «right» people. The ability to increase business processes efficiency is provided by robots and automation.

All these developments are based on an ever-increasing stream of data that is collected, stored and analyzed. Using data, some companies have revolutionized new services to improve and simplify human life: search engines, communications, e-commerce, booking systems and more.

However, most of the companies did not achieve such success. The reason is that companies  struggle to manage their data and most of it is not used. Accordingly, monetization does not occur.

The first block in data management is storage. An incorrect strategy to overcome this task, or its complete absence, may lead to other problems in the future. Data volume is constantly growing, so companies need to clearly understand what data is important.

At the moment, cloud services offer to store almost unlimited information amounts. However, there are certain difficulties here. For example, data with a high level of confidentiality or regulatory burden cannot be hosted outside the enterprise; some data requires instant access from anywhere in the world; some data requires routine archiving. Also, there is a need for data auditing to determine data relevance and compliance with international regulations. To perform these functions smoothly, it’s necessary to know where the data is located, how many copies exist, and how to access it.

The process of obtaining reliable data can be achieved with fast and highly available storage systems. Modern business analytics involves moving and sorting large data amounts to provide business users and customers with flexible functionality. Including the system must be supported by encryption and security.

Intelligent data warehouse

To achieve maximum speed, stability and security, modern storage systems, including the IBM FlashSystem, use solid-state non-volatile media. Artificial intelligence technologies usage enables intelligent management of data storage and access, which can increase speed and minimize the likelihood of errors and data loss. So, the data with the most frequent predictable access will be ready for work and will be queued.

One of the key requirements for a storage system is resilience. The goal of many businesses today is to build analytics-driven internal processes and customer relationships. In such model, it is impossible to allow these processes to stop as a result of problems with the data flow or infrastructure. Modern storage systems offer the ability to quickly copy and replicate corporate data. Ensuring data integrity is often critical. In this case, 2 or more identical copies of data can be synchronized in different places, and in a situation of an unforeseen failure, it can be restored with practically no data loss.

Rapidly changing data management

The UK Met Office has tackled the challenge of implementing the infrastructure needed to handle rapidly changing data. The information is used to determine weather changes, climate research, and seasonal trends. To do this, 300 million weather-related data points are collected, analyzed daily, and made available to customers. This happens twice to eliminate the risk of interrupting the data flow.

To support this process, a hybrid cloud strategy was developed based on the IBM FlashSystem. The storage provides a high level of compression, which is a cost-effective solution. It also contributes to the creation of a high-performance data infrastructure, which is necessary to transfer information from internal servers to the public cloud and clients.

Another example is the Archdiocese of Salzburg, which needed a solution to provide services more efficiently (support, outreach to the community and parishioners, access to many historical documents and literature). The Archdiocese was able to increase response times 10 to 20 times by moving away from mechanical disk storage in favor of solid-state, non-volatile systems.

Data is an important part of business assets. All data decisions must be smart and effective. The storage process should be considered as a key element of the data management strategy, along with the collection and analytics processes.

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 Intelligence, a 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.

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