performance improvement

«There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know»

Donald Rumsfeld

Rumsfeld Matrix

During a briefing on the Iraq War, Donald Rumsfeld divided information into 4 categories:

known known
known unknown
unknown known
unknown unknown

These differences became the basis of the Rumsfeld matrix (a decision-making system that displays and evaluates different degrees of certainty and uncertainty).

4 quadrants of the Rumsfeld matrix

Known knowns: These are facts that we know and understand. It is our knowledge base that provides a solid basis for decision making;
Known unknowns: These are facts that we know about, but do not fully understand. These are gaps in our knowledge that we need to address through research, investigation or expert consultation;
Unknown knowns: These are facts that we don’t realize we know. Such information is stored in our subconscious, ignored and considered unnecessary. Disclosure of such information can be beneficial and lead to certain breakthroughs in the decision-making process;
Unknown unknowns: These are facts that we do not know and cannot predict. They are the most significant source of uncertainty and risk, as they can lead to surprise and disrupt plans.

Rumsfeld Matrix effectiveness

Managing uncertainty: the matrix helps to navigate uncertainty while breaking it down into manageable pieces. Known unknowns help to identify assumptions that need to be tested. The unknown knowns help to access hidden knowledge and ideas that will contribute to a better understanding of a situation;
Improved decision making: Using a matrix helps prioritize and focus on more important sources of uncertainty. Systematic elimination of such uncertainties makes it possible to make more informed decisions and effectively manage risks;
Teamwork: The matrix promotes open and honest communication among team members. As they work, team members effectively collaborate with each other, identify, resolve known unknowns and unknown knowns, share knowledge, and discuss assumptions.

Rumsfeld Matrix using

Quadrants definition:

decision context – it is necessary to identify the decision context, objectives, constraints and key stakeholders. This stage is the basis for further analysis and ensures that all team members understand the goal and parameters of the solution;
matrix creation – it is necessary to create a 2×2 matrix with columns «Known» and «Unknown», and the same rows. The result will be 4 quadrants: «known known», «known unknown», «unknown known», «unknown unknown»;
«known knowns» quadrant – all the facts, assumptions and variables that are known and understood by all team members. Since this information is the basis of the decision-making process, it must be accurate and complete;
«known unknowns» quadrant – all the factors, issues, and variables that team members are aware of but do not fully understand. Identified gaps and potential risks can be closed through additional research and study. This stage requires honesty and thoroughness, because this is where the assumptions that need to be focused on are determined;
«unknown knowns» quadrant – this stage is quite complex, which involves identifying hidden or missed ideas and knowledge. Active, structured discussion and reflection on past experiences will help identify knowledge and perspectives. It is also advisable to turn to turn to experts and consult on certain issues;
«unknown unknowns» quadrant – at this stage the unknown should be assessed. Since it is impossible to predict all unknown unknowns, it is important to recognize their existence and the potential risk of their occurrence. It is necessary to plan for different scenarios, develop a contingency plan, establish monitoring and feedback mechanisms. This approach will help to improve response to and management of unknown unknowns as they arise.

Actions:

Prioritizing and eliminating «known unknowns». At this stage, should focus on the «known unknowns» quadrant, prioritizing all components of the quadrant based on their potential impact on the decision. Information obtained through additional research and consultation must be entered into the matrix;
Using «unknown knowns». Previously hidden knowledge and experience can lead to new ideas and alternative solutions. Integrating all the ideas will help to make more informed decisions;
Monitoring and responding to «unknown unknowns». This quadrant will always exist. Regular monitoring of the decision-making environment and adaptation to unforeseen situations should be organized, feedback loops and action plans should be introduced in such situations. This will help to remain flexible and respond quickly to emerging uncertainties;
Making decisions. Based on the completed matrix and available information, decisions must be made. Monitoring and analyzing the results of its implementation will help to draw lessons and experience. The matrix can be updated as needed and used for subsequent decision making.

Data has become one of the most important business assets for any size and activity company. At the moment, there are many data use cases. However, each company must choose its own option that will bring the greatest benefit. To be successful today all companies regardless of size or industry must have a data strategy. It is important to understand exactly what data is needed to achieve goals and improve business results.

With so many potential data use cases it’s easy to get confused. But the task of choosing a scenario should not be left to chance. Below are some tips to help leaders prioritize and create an effective enterprise data strategy.

Brainstorm

First of all, it is necessary to outline the desired result: «What exactly does the business want to achieve?», «What are the key business tasks and goals?.

There are 4 main uses for data:

Understanding your customers: build a customer profile, identify their needs and track their purchases, identify market trends;
Product or service improvement: an individual approach to the client, offering an improved product or service creates company value and allows to increase customer loyalty, as well as attracting new ones;
Internal business processes improvement: business operations optimization, business management improvement, marketing improvement, production processes optimization, etc.;
Monetization: data sale or lease. For example, the multinational financial corporation Mastercard collects data on the shopping habits and activities of its users, and then sells it to other companies.

Detailing use cases

Every data project that has been brainstormed needs to be drilled down:

The strategic goal definition. What business problem needs to be solved?
Success measurement. How will be determined the success of data use? In order to understand how data helps to increase productivity, increase sales, improve company efficiency, etc., it is necessary to define KPIs;
Determining the owner of the data use case (data client): marketing department, operations department, sales department or other users;
Data source determination: where it will come from and whether there are any rules/requirements for their use (legal requirements, GDPR, etc.);
Understanding the process of transforming data into ideas: analytical approaches, identification of implementation problems, the need for certain skills for development.

Define the use case

Having passed the previous stages, it is necessary to determine the most suitable for a particular business. This approach will identify data priorities, cross-cutting issues, requirements and goals. You should start with the current business strategy, the most significant challenges and opportunities. Depending on the ambition and scale of the data processing strategy, it is necessary to choose from 1 to 5 use cases. They will also be the most strategic and long-term ones.

After that, it is worth identifying 1 – 3 «quick goals» that can be achieved quickly and can show value. A quick result will demonstrate the effectiveness of the data processing strategy and allow to get team members’ support.

Now the world is going through the 4th industrial revolution, the main engines of which are data, Artificial Intelligence and the Internet of things. As a result, the surrounding world is transformed into one large information system. In addition to the large number of technologies that are involved in this revolution, an important condition for progress is the different ways all these technologies interacting. Technologies development continues and it will affect every field of activity and company anyway.

Let’s take a look at 10 major technology trends that have an impact on a company’s success.

Ubiquitous computing

Today, a mid-range smartphone is more powerful than a super-powerful computer 10 years ago, microchips have become smaller, devices are smaller, lighter and more powerful. Computing advances in the future will come from software and algorithms, quantum computing, new forms of digital storage (like DNA storage).

Connections

At the moment, the Internet of Things (IoT) is developing very actively. This gives the impression that each device can connect to the Internet as well as collect and transmit data. According to the forecast by 2030, there will be at least 50 billion IoT devices installed in the world. This ability to connect places and things to the Internet can change many areas (education, manufacturing, medicine, etc.). Working with IoT data opens a unique understanding of the real actions of the client and employee.

World datafication

People generate huge data amounts every day without even thinking about it. Almost every human action leaves behind a digital footprint. This contributed to the data storage methods development. The challenge for companies is to ensure that information assets are properly protected and confidential. Business data can serve as the main source for an improved product or service creating, business processes optimizing, etc. However, this requires a strategy for transforming data into analytic data. Artificial Intelligence can become an important tool for improving data literacy.

Artificial Intelligence

AI development is very dynamic that allows modern machines to perform different tasks instead of humans. AI can also improve internal business processes by automating or helping specialists perform specific tasks, as well as in the decision-making process.

Augmented reality

Augmented reality (XR) is an umbrella term that includes a range of immersive technologies (virtual, mixed, augmented reality).

Trust in digital technology

Digital trust is the trust that users place in organizations in building a secure digital world. Each user must be confident in the security, reliability and ease of transactions or other interactions.

3D printing

3D printing provides an opportunity to redefine the way things are made. Manufacturers also get another way of making a product in an unconventional way. This allows them to streamline their manufacturing process, create custom products, and cut costs and waste.

Genome editing and synthetic biology

Uncovering genetic mysteries will help to find new ways to understand and control them. Gene editing techniques such as CRISPR can make significant advances in the fight against various diseases, improve the vitality of plants, and produce new synthetic substances, that can replace fossil fuels, plastics, animal products.

Nanotechnology and materials science

Nanotechnology means controlling matter on a tiny scale (atomic and molecular). Nanotechnology can be used to manage and improve products and components.

New energy solutions

Renewable energy sources (wind, solar) have now become more efficient and affordable. In the future, there are 2 new energy sources – green hydrogen and nuclear fusion. New sources can be an important, safe and environmentally friendly solution to energy needs.

Data by itself is not too valuable. What makes it valuable is analytics and insights. Data analytics helps to get the current situation, draw the right conclusions and make decisions that will lead to more efficient business activity and growth.

Let’s consider a car example. Often, we do not think about how it works, but we know that it can go wrong. It is possible to identify the causes and prevent them using analysis. It is enough to collect 2 datasets: the first is about car workflow and the second is about time when the car fails. Such dataset analyzing will help to understand what actions lead to failure and identify ways to prevent recurrence in the future.

Descriptive analytics

Data scientists call the analytics in the example above «descriptive». The result of this analytics type is the description of «what?», «when?» and «why?» happened. However, it does not offer possible solutions.

Now modern companies mostly use descriptive analytics. Specialists get access to the full amount of information in the form of graphs, charts, tables, reports that show the recurrence number of a particular event. This can be interaction with a client, a mechanical failure, sales, etc. Next, the specialist’s task is to analyze all information and develop the action plan.

A good example of descriptive analytics is Google Analytics. This tool perfectly visualizes indicators of site traffic, its load, user behavior on the site and much more. Descriptive analytics can be useful when used strategically.

Predictive analytics

The goal of many companies that adopt artificial intelligence and machine learning technologies is to enable predictive analytics.

An idea if predictive analytics is to analyze data about what has already happened and make predictions. Let’s go back to the car example. There is problems report from January to March. Prediction algorithm provides information on how and when a machine may fail in the future.

The result of predictive analytics is a set of probabilities that do not give 100% confidence that an event will definitely happen in the future. However, having such information, it is possible to prepare, check the availability of spare parts for repairs and develop a «plan B».

Predictive analytics is actively used by banks and lenders to assess risks. In consequence of analytics bank employees receive an estimate of the payment likelihood by a particular applicant and can compare it with risk thresholds.

Until recently, only advanced companies could implement analytical technologies and evaluate benefits. Now these companies are moving to «prescriptive analytics».

Prescriptive analytics

So, predictive analytics provides an answer to the question «what can happen», but does not answer the question «what to do to obtain the optimal result?» The next step in analysis is prescriptive analytics.

The predictive analytics system provides a number of possible outcomes. This can be effective when the specialist exactly knows the effective solution. For example, choose a specific action to increase sales. However, if the goal is to increase revenue in general, it is necessary to know the most effective measures package. This is where prescriptive analytics can help.

Let’s take autonomous vehicles as an example. The car must «know» that the fastest left/right turn can be lengthened by heavy traffic for a certain period. In this case, it is necessary to choose the best option and «register» it to the computer that drives the car.

With prescriptive analytics, the person driving the machine can know not only the cause of the failure, but also the best way to minimize such situations.

The 3 levels of analytics described above are fundamental to the automation process. Until recently, advanced predictive and prescriptive analytics technologies were prohibitively expensive for most companies. Now there are a large number of analytical tools and platforms that small organizations can afford. Gradually progressing through the analytics levels contributes to a successful digital transformation.

Successful business is based on information. Information quality and timeliness are the key of effective activity. Data analytics gives the business many opportunities: right goals setting and the most accurate result achievement, forecasts creating, trends identifying, strategy development, possible risks identifying and their consequences preventing or minimizing.

Among other things, analytics is also important in determining the company’s website effectiveness. Working with data on site performance and marketing strategy from multiple sources enables management to determine the company’s ability to meet expectations.

Website traffic check and comparative analysis with competitors’ websites provide a clear picture of business development on the Internet. The lack of this information makes it quite difficult to run business.

Let’s consider several reasons why analytics is important in the business management world:

The target audience knowledge provides further movement understanding

The target audience is rarely stable and predictable, so its analysis is critical for strategy development, plans generation and goals setting. Such analysis may contain information about the age categories of users, their preferences, the platforms they use to browse the website. Tracking online users’ behavior makes it possible to timely identify various changes, new trends and promptly respond to them. Therefore, it optimizes the business marketing strategy and leads to effective operations.

Promotes web optimization

There are many indicators, by determining which it is possible to say exactly how effective the site is. For example, a high bounce rate means «something» on the main page that prevents users from navigating further to other website pages. The reasons can be different: too much information on the main page, long page load time. Also, the reason may lie in incorrect marketing – the user’s request does not match the site information content. With the help of analytics, you can identify all the causes, eliminate them and set up correct and effective marketing.

Helps reduce stress levels

Uncertainty and unexpectedness always provoke stress and frustration. Nescience and understanding lack of how to act can be a big problem for business owners. Failures can happen with a strategy and possession of all the information. However, understanding the reasons why a particular situation happened minimizes stress level and makes it possible to determine alternative development way. The analytics essence is to provide all the necessary tools to create an effective marketing and optimization strategy.

Analytics is the main tool for modern business management. Analytical tools implementation and usage make it possible to understand the business, track causal relationships and optimize the company’s activities.

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:

In Processing Situational and Textual Data

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

Changing the way our business functions

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.

Simplifying performance across various domains

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:

Structured process

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.

Defined or definable workflow

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

Use of multiple applications or tools

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.

No subjectivity

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.

High volume / low to medium complexity

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.

Partial automation

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:

Provide clear direction for the project and how it overlaps with the organization’s overall strategy
Ensure the project is on time, on budget and within the scope
Secure project resources
Provide feedback on status reports
Ensure the necessary stakeholders are involved
Champion the project at the executive level to secure buy-in

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:

Designing and establishing a roll-out plan with effective governance, security, and controls.
Ensuring focus and prioritization on delivering the “high value” projects.
Coordination and control of projects and resources.
Reporting outcomes and benefits.
Resource management.
Supplier management.

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.

Due to the strengthening of quarantine measures, more and more people are forced to work remotely. However, for those who are used to working in the office, the home environment doesn’t inspire to work, rather the opposite. Moreover, many have children and families, so it seems impossible to enter the working mode and not be distracted. For Denys Smakovskyi, the founder and CEO of DataLabs, this is familiar, that’s why, based on his own experience and past mistakes, he shared with us tips that will help you work at home more efficiently.

1. Get some fresh air

In the morning you need to take a walk, and, in case of self-isolation, some physical exercises or just drink coffee on the balcony. The main requirement is to breathe in some fresh air. This allows you to rev up your brain, structure thoughts and plans for the day.

2. Organize your own workplace.

Having your own workplace is a must.

Everyone around you should know that when you are there, you are working, and you should never be disturbed.
A separate room would be great, but if it is not an option, find a separate corner in which you can set your own rules and boundaries.
There should also be a specific rule on how to reach you in that zone – either through some messenger, or somehow giving a signal and waiting for your response. It is very important not only to inform others about these rules, but also to strictly adhere to them, filtering everything that does not fall under these signals (emergency situations are a separate issue). When you are in the working room, it is forbidden for someone to simply run into it – they can calmly ask something from the doorway. If the door is closed, they need to knock, and if you are too busy and do not answer, they have to message you. If there is no separate room or when you are traveling, the headphones act as a “flag” – if they are on you, you are busy and there may be a call. In addition, the camera should always be placed in such a way as to minimize the possibility of family members getting into its lens.

There is no guarantee that none of these rules will be broken – they get broken regularly. But the number of problems will decrease substantially

The Internet in your work area must be perfect.
A comfortable table and chair are also something that you must take care of.

3. Stay away from social media

Not everyone succeeds in this. However, it is better to log out of your social media accounts during working hours. If you can’t, or waiting for some materials or monitoring some situation, open it on an additional screen, or set a timer, for example 50-10, when you work 50 minutes and, if you have time and inspiration, spend 10 minutes on social media.

4. Cook in time

It is best if you have precooked meals during working time or that someone prepares it for you. If you cook yourself, then make sure that the food is available within 5-10 minutes, otherwise if you’re on a roll, you will be too lazy to cook it. When someone else is cooking, they have to offer you a meal break 30 min prior to that and remind you 5 minutes before and 5 minutes after when this time has come. Also ask to be aware that you can become very invested in the work process, and there is nothing to worry about if you have to reheat food in the microwave. Be sure to remember – food, delicious and of high-quality to boot, is key to your health and productivity.

5. Hold meetings

Meetings, many and different, with voice and video, are one of the best ways to maintain a working mood and team spirit. In our company, we use Microsoft Teams for this. If you want to discuss something, invite people to a call. It is better to include a video (more on that below). All this will help you to adapt to remote work, not to lose touch with the team and maintain personal relationships with colleagues.

6. Do not work in pajamas or naked (seriously)

It’s very tempting. To jump out of bed, sit behind a desk in your underwear with a mug of coffee and get to work. It only seems like a good idea, but in fact it has a very negative impact on your morale and productivity, increases frustration and procrastination. Your body blurs the liners between personal life and work, and without these boundaries it is very easy to fall into the existential questions like “Why am I so pathetic?”, “What’s the point of it all?” In addition, it is very easy to blur the boundaries of a workday from 7-9 hours to 16-18 hours a day, thus neglecting other areas of life. Work-Life balance is absolutely necessary to maintain, even when you work remotely. That is why it is best to have various video meetings with colleagues. This will oblige you to freshen up a little.

7. Don’t devote your whole life to work

The most important thing is to limit your working time, and, during after hours, pay attention to other aspects of your life and feelings. Cooking, spending time with children, partners, friends, painting, replanting flowers, doing something around the house are many other things that involve various aspects of your life and physical condition. After all, your life is not only work. Your work is the basis for your life.

Bonus Tip: Refrain from drinking alcohol during working hours

You think “why not,” but in fact, even the smallest amount of alcohol can unsettle you and make you unproductive. It is necessary to clearly set aside time for a glass of good wine, for example, after 18:00 on Friday and until 23:00 on Sunday.

These tips are extremely important and effective, because they were based on many years of experience of other people. Our common goal is to adapt as much as possible and strengthen our own productivity in order to save ourselves, help the company and overcome the current crisis as successfully as possible.

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.

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.

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.