How to Overcome Challenges in Adopting Data Analytics

AJINKYA | April 20, 2020

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Achieving organizational success and making data-driven decisions in 2020 requires embracing tech tools like Data Analytics and collecting, storing and analysing data isn’t.The real data-driven, measurable growth, and development come with the establishment of data-driven company culture. In this type of culture company actively uses data resources as a primary asset to make smart decisions and ensure future growth.

Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Let’s explore ways for overcoming common blockers, and elements used in successful analytics adoption strategies.

Table of Contents:
- AMM: Analytic Maturity Model
- What are the blockers to achieving a strategy-driven analytics?
- What are the adoption strategies to achieve an analytics success?
- Conclusion

AMM: Analytic Maturity Model

The Analytic Maturity Model (AMM) evaluates the analytic maturity of an organization. The model identifies the five stages an organization travels through to reach optimization. Organizations must implement the right tools, engage their team in proper training, and provide the management support necessary to generate predictable outcomes with their analytics. Based on the maturity of these processes, the AMM divides
organizations into five maturity levels:
- Organizations that can build reports.
- Organizations that can build and deploy models.
- Organizations that have repeatable processes for building and deploying analytics.
- Organizations that have consistent enterprise-wide processes for analytics.
- Enterprises whose analytics is strategy driven.


What are the blockers to achieving a strategy-driven analytics?

- Missing an Analytics Strategy
- Analytics is not for everyone
- Data quality presents unique challenges
- Siloed Data
- Changing the culture

What are the adoption strategies to achieve analytic success?

• Have you got a plan to achieve analytic success?

The strategy begins with business intelligence and moves toward advanced analytics. The approach differs based on the AMM level. The plan may address the strategy for a single year, or it may span 3 or more years. It ideally has milestones for what the team will do. When forming an analytics strategy, it can be expensive and time consuming at the outset. While organizations are encouraged to seek projects that can generate quick wins, the truth is that it may be months before any actionable results are available. During this period, the management team is frantically diverting resources from other high-profile projects. If funds are tight, this situation alone may cause friction. It may not be apparent to everyone how the changes are expected to help. Here are the elements of a successful analytics strategy:

• Keep the focus tied to tangible business outcomes

The strategy must support business goals first. With as few words as possible, your plan should outline what you intend to achieve, how to complete it, and a target date for completion of the plan. Companies may fail at this step because they mistake implementing a tool for having a strategy. To keep it relevant, tie it to customer-focused goals. The strategy must dig below the surface with the questions that it asks. Instead of asking surface questions such as “How can we save money?”, instead ask, “How can we improve the quality of the outcomes for our customers?” or “What would improve the productivity of each worker?” These questions are more specific and will get the results the business wants. You may need to use actual business cases from your organization to think through the questions.

• Select modern, multi-purpose tools

The organization should be looking for an enterprise tool that supports integrating data from various databases, spreadsheets, or even external web based sources. Typically, organizations may have their data stored across multiple databases such as Salesforce, Oracle, and even Microsoft Access. The organization can move ahead quicker when access to the relevant data is in a single repository. With the data combined, the analysts have a specific location to find reports and dashboards. The interface needs to be robust enough to show the data from multiple points of view. It should also allow future enhancements, such as when the organization makes the jump into data science.
Incorta’s Data Analytics platform simplifies and processes data to provide meaningful information at speed that helps make informed decisions.

Incorta is special in that it allows business users to ask the same complex and meaningful questions of their data that typically require many IT people and data scientist to get the answers they need to improve their line of business. At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owner.

- Tara Ryan, CMO, Incorta

Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, the customer-facing processes have areas where it is easier to collect data and show opportunities for improvement.

• Ensure staff readiness

If your current organization is not data literate, then you will need resources who understand how to analyze and use data for process improvement. It is possible that you can make data available and the workers still not realize what they can do with it. The senior leadership may also need training about how to use data and what data analytics makes possible.

• Start Small to Control Costs and Show Potential

If the leadership team questions the expense, consider doing a proof of concept that focuses on the tools and data being integrated quickly and efficiently to show measurable success. The business may favor specific projects or initiatives to move the company forward over long-term enterprise transformations (Bean & Davenport, 2019). Keeping the project goals precise and directed helps control costs and improve the business. As said earlier, the strategy needs to answer deeper business questions. Consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide an analytics sandbox with access to tools and training to encourage other non-analytics workers (or citizen data scientists) to play with the data. One company formed a SWAT team, including individuals from across the organization. The smaller team with various domain experience was better able to drive results. There are also other approaches to use – the key is to show immediate and desirable results that align with organizational goals.

• Treating the poor data quality

What can you do about poor data quality at your company? Several solutions that can help to improve productivity and reduce the financial impact of poor data quality in your organization include:

• Create a team to set the proper objectives

Create a team who owns the data quality process. This is important to prove to yourself and to anyone with whom you are conversing about data that you are serious about data quality. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. To gauge performance, you need a set of metrics to measure the performance. After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of "good data in, good data out" and "bad data in, bad data out." To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports those business questions.

• Focus on the data you need now as the highest priority

Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of Successful Analytics Adoption Strategies, continuing to affect that data. As you decide which data to focus on, remember that the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is (Wessel, 2016).

• Automate the process of data quality when data volumes grow too large

When data volumes become unwieldy and difficult to manage the quality, automate the process. Many data quality tools in the market do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that although the tools help with the organization and automation, the right processes and knowledge of your company's data are paramount to success.

• Make the process of data quality repeatable

It needs regular care and feeding. Remember that the process is not a one-time activity. It needs regular care and feeding. While good data quality can save you a lot of time, energy, and money downstream, it does take time, investment, and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps.

• Beware of data that lives in separate databases

When data is stored in different databases, there can be issues with different terms being used for the same subject. The good news is that if you have followed the former solutions, you should have more time to invest in looking for the best cases. As always, look for the opportunities with the biggest bang for the buck first. You don't want to be answering questions from the steering committee about why you are looking for differences between "HR" and "Hr" if you haven't solved bigger issues like knowing the difference between "Human Resources" and "Resources," for example.

• De-Siloing Data

The solution to removing data silos typically isn’t some neatly packaged, off-the-shelf product. Attempts to quickly create a data lake by simply pouring all the siloed data together can result in an unusable mess, turning more into a data swamp. This is a process that must be done carefully to avoid confusion, liability, and error.
Try to identify high-value opportunities and find the various data stores required to execute those projects. Working with various business groups to find business problems that are well-suited to data science solutions and then gathering the necessary data from the various data stores can lead to high-visibility successes.
As value is proved from joining disparate data sources together to create new insights, it will be easier to get buy-in from upper levels to invest time and money into consolidating key data stores. In the first efforts, getting data from different areas may be akin to pulling teeth, but as with most things in life, the more you do it, the easier it gets.
Once the wheels get moving on a few of these integration projects, make wide-scale integration the new focus. Many organizations at this stage appoint a Chief Analytics Officer (CAO) who helps increase collaboration between the IT and business units ensuring their priorities are aligned. As you work to integrate the data, make sure that you don’t inadvertently create a new “analytics silo.” The final aim here is an integrated platform for your enterprise data.

• Education is essential

When nearly 45% of workers generally prefer status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to their size or outside forces.

There’s also a culture shift required - moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness.

- Walter Storm, the Chief Data Scientist, Lockheed Martin

Companies spend a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy.

If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the data to move forward with requests that will support conclusions that will be used to make critical decisions across the business.

These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward. Once senior leadership uses data to make decisions, everyone else will follow their lead. It is that simple.


The analytics maturity model serves as a useful framework for understanding where your organization currently stands regarding strategy, progress, and skill sets.
Advancing along the various levels of the model will become increasingly imperative as early adopters of advanced analytics gain a competitive edge in their respective industries. Delay or failure to design and incorporate a clearly defined analytics strategy into an organization’s existing plan will likely result in a significant missed opportunity.



Stealth-mode startup building a public cloud SaaS data platform.


What are the Benefits of Data Modeling for Businesses?

Article | January 21, 2022

Businesses that are data-driven are well-known for their success, as data is widely considered to be a company's most valuable asset. Understanding data, its relationships, and the law requires the use of data modelling techniques. Sadly, people who are not familiar with data modelling best practises see them as a pointless documentation exercise. In the eyes of others, it is a hindrance to agile development and a waste of money. A data model is more than just documentation because it can be implemented in a physical database. Therefore, data modelling is not a bottleneck in the development of an application. Due to these benefits, it has been proven to improve application quality and reduce overall execution risks. Data modeling reduces the budget of programming by up to 75%. Data modeling typically consumes less than 10% of a project budget. Data Modelling- Today’s Scenario Data models methodologies for data modelling have existed since the dawn of time. At the very least, it's been around since the dawn of the digital age. In order for computers to deal with the bits and bytes of data, they need structure. Structured and semi-structured data are now part of the mix, but that doesn't mean we've reached a higher level of sophistication than those who came before us in the field of computing. As a result, the data model lives on and continues to serve as the foundation for the development of advanced business applications. Today's business applications, data integration, master data management, data warehousing, big data analytics, data Lakes, and machine learning require a data modeling methodology. Therefore, data modeling is the foundation of virtually all of our high-value, mission-critical business solutions, from e-Commerce and Point-of-Sale to financial, product, and customer management, to business intelligence and IoT. "In many ways, up-front data design with NoSQL databases can actually be more important than it is with traditional relational databases [...] Beyond the performance topic, NoSQL databases with flexible schema capabilities require more discipline in aligning to a common information model." Ryan Smith, Information Architect at Nike How is Data Modelling Beneficial for Businesses A data model is similar to an architect's blueprint before construction begins. The visual manifestation of a development team's understanding of the business and its rules is data modeling. The data modeling methodology is the most efficient way to collect accurate and complete business data requirements and rules, ensuring that the system works as intended. In addition, the method raises more questions than any other modeling method, resulting in increased integrity and the discovery of relevant business rules. Finally, its visual aspect makes it easier for business users and subject matter experts to communicate and collaborate. Let us look into some of the core benefits of data modeling for businesses. Enhanced Performance Following Data modeling techniques and best practices prevents the schema from endless searching and give results faster, resulting in a more efficient database. The data model's concepts must be concise to ensure the best performance. It's also crucial to accurately convert the model into the database. Higher Quality Data Data modeling techniques can make your data precise, trustworthy, and easy to analyze. Inaccurate data and corruption are even worse than application errors. Data can be adequately understood, queried, and reported on as a good data model defines the metadata. Developers can foresee what can lead to large-scale data corruption before it happens because of the visual depiction of requirements and business rules. Reduced Cost Effective data modeling techniques detect flaws and inconsistencies early in the process, making them significantly more accessible and less expensive to fix. As a result, data models allow you to design apps at a reduced cost. Data modeling often takes less than 5%-10% of a project's budget, and it can help lower the 65-75 percent of a project's budget that is usually allocated to programming. Better Documentation By documenting fundamental concepts and language, data model methodologies lay the groundwork for long-term maintenance. The documentation will also aid in the management of staff turnover. As an added bonus, many application providers now provide a data model upon request. For those in the information technology field, it's common knowledge that models are a powerful tool for explaining complex ideas in a simple and straightforward manner. Managed Risk An application database that contains numerous related tables is more complex and thus more prone to failure during development. On the other hand, data model techniques quantify software complexity and provide insight into the development effort and risk associated with a project. Therefore, the model's size and the degree of inter-table connectivity should be considered. Summing up Any business can benefit greatly from data modelling methods and techniques. To the untrained eye, data modelling may appear to be distinct from the type of data analytics that actually add value to a company. In order to make data storage in a database easier and have a positive impact on data analytics, data modelling is an essential first step. Frequently Asked Questions What is data modeling? In software engineering, data modelling refers to the use of specific formal techniques to develop a data model for an information system. This is used to communicate between data structures and points. Which are the five crucial data modeling types? The five crucial data modeling types are Conceptual data model Physical data model Hierarchical data model Relational data model Entity-relationship (ER) data model

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7 Apps to Learn and Practice Data Science Effectively

Article | January 19, 2022

Rapid digitization is generating an enormous amount of data every second. The field of data science employs highly unstructured data to draw meaningful insights that can help businesses make smart decisions. It is one of the ever-evolving technologies that is reshaping the landscape of the digital world. To be a data scientist, one must have the skills, methods, and knowledge of top-notch algorithms that process and execute data efficiently. This article highlights seven apps that can help you grow in your career as a data scientist. 1. SmartWindows SmartWindows is a productivity app for Windows 10 and Windows 11 users. It is a handy desktop application for data scientists when it comes to getting a 360-degree view of multiple desktop files. A data scientist works on multiple data files at a time. Owing to this, excessive switching between apps becomes very inefficient and time-consuming. SmartWindows eliminates that overhead by allowing users to arrange multiple desktop apps and data files on-screen and save the screen arrangement in the SmartWindows profile. Once saved, users can restore the profile at any time, whereby all files will be restored at the exact same position and same windowsize on the desktop screen will be a single click. SmartWindows allows users to create unlimited profiles and up to six displays. It auto-restores the screen arrangements on one or many screens. You can work on multiple work zones and save and restore them at any time. SmartWindows supports multiple browsers such as Google Chrome, Firefox, Microsoft Edge, etc., and restores dozens of tabs all with one click. It also restores Smart Layouts on Windows 11 without the hassle of creating new layouts before getting started on work. 2. QPython QPython is a script engine that runs Python programs on Android. This mobile app contains all the essential components that will help you learn python. Data scientists need to play a lot of data that is hard to interpret and evaluate using manual methods. For complex unstructured data, it is difficult to make decisions, and therefore, for all the preprocessing and advanced techniques, Python is a must-learn programming language to study. QPython gives users leverage to learn Python using their mobile phones. It contains an editor, a console, an interpreter, a terminal, an explorer, and the SL4A library for Android. The app comes with two basic modules, of which one is for beginners called QPython OL and the other is for experienced Python users called QPython 3x. If you are a beginner, you must choose QPython OL as it provides all the basic understanding and programming techniques to get started with. The app does not require an active internet connection, which means you can open and learn it anytime anywhere you want. 3. DataCamp DataCamp is a learning platform to enhance data science skills online. The platform has a variety of programs and skills you can learn from. The platform follows a comprehensive hands-on experience in which you learn, practice, and apply the techniques of data science to real-world problems. It follows a complete curriculum of advanced and non-coding essentials that can help users learn data science and machine learning. It offers hundreds of video courses on different subjects. Practice can be done through various interactive exercises that can help you improve your data science skills. 4. Elevate Elevate is a cognitive training mobile app for Android and iOS. It helps users enhance their mind’s processing speed, speaking abilities, and problem-solving skills. They offer personalized training sessions that include exercises. It is a suitable tool for data scientists to strengthen their analytical and communication skills. Elevate consists of more than 35 games that enhance self-confidence, productivity, math, writing, and learning skills. 5. Math Workout It is an educational application to test the analytical skills of users, keeping their minds healthy. It involves multiple exercises and tests to increase the processing power of the mind through mathematics. Mental arithmetic helps enhance data science skills. The Math Workout has some cognitive games and mental exercises in more than ten languages that interpret the cognitive psychology of users and train their brains accordingly. The app would make you perform calculations at your fingertips, allowing you to process tough calculations in a matter of seconds. 6. Programming Hub The Programming Hub is a platform developed for developers. It consists of more than 5000 computer programs and 20+ courses for programmers to practice and learn programming languages. Whether you are a beginner or an expert, the Programming Hub offers a wide range of programming courses to help you with problem-solving skills with multiple programs and software frameworks. The app specializes in providing programs for artificial intelligence, R programming, C, C++, C#, Java, and more. 7. NeuroNation As the name suggests, NeuroNation is a brain training app that is designed to improve the brain activity of users through various exercises. It enhances the processing power of the brain and the logical thinking capability of users. It has a set of 60 different exercises, games, and activities. It is a website and an app for both Android and iOS. It has extraordinary features to increase scientific basics, personalization, fun, and motivation among the learners.

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How Data Science Industry is changing - A view from 2022

Article | January 12, 2022

The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments. AI, AI everywhere AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks. Data Centric is the trend With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. In model-centric approach, data is kept constant while the model is tweaked to adjust to the data and get a good result. What’s the drawback of this? Not performing good on real world data. But, then why the model gave good results? That is because, model completely got adjusted to the data you gave instead of generalizing to the real-world problem. This issue came in the recent past, thus the trend of Data-centric came in. Experts like Andrew Ng often hosts talks and campaigns to shift the focus on ML Practitioners and Industries to Data rather than model. According to him, “data is like food for the model”. Data Engineers on Rise As a trailing topic to the previous one, Data Engineers will have a good rise in the coming future. As seen before, there is a copious amount of existing data and is getting generated at an un-imaginable rate. This might sound good since, “more is good” for analytics, but comes with the disadvantage of difficulty in ensuring data quality. As more data comes in, maintaining quality can be challenging. One reality here is that not all times a cloud pipeline can be used to ensure data quality and automated cleaning. There are times where raw data is logged as it is. This calls for more data engineers to perform cleaning and tuning of data to ensure it meets quality standards. With the oncoming of Data-Centric AI approach, this will have a great hype in the coming recent time. And speaking from the Industry Point of View, Data is one of the core part of Data Science and has no replacement. No good data, no good results and the crash of Data Science. “Artificial” becoming more on the point for AI AI started as a research topic long back. It all started like a big-bang – Basic linear regression to Neural Networks. The latest AI algorithms can now see like a human (Computer Vision), speak like a human (Natural Language Processing) and assist in decision making (Inferencing using Models). The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has. Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity. More opportunities coming up As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.

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Effective Ways to Prevent Data Breaches

Article | November 26, 2021

Data breach prevention is going to be the need of the hour as cybercrime continues to grow. Cybercrime is a growing threat to businesses of all sizes. Due to this unprecedented time many companies shifted to work-from-home model. Statics show data breaches are on a rise and can have devastating long-term financial set-back or reputational repercussions to your organization. As a result, businesses must ensure that their data is secure to avoid substantial loss or theft. As data breaches happens in different ways, there is no such thing as a one-size-fits-all remedy. Security needs a multifaceted approach to be effective. In this article we’ll find out different ways prevent data breaches. Impact of a data breach on businesses A data breach can destroy a business, especially for small and medium-sized businesses (SMB). Data is a valuable asset for any business especially, the data related to customers and payments. Cybercriminals find this data valuable. Lack of planning and security creates vulnerabilities for criminals to exploit. It is estimated that 60% of small and medium-sized enterprises will close within six months after the attack. Larger businesses or agencies, on the other hand, will survive. Nevertheless, they too will suffer the consequences. A data breach can impact businesses in the following ways; Financial Businesses must compensate for both immediate and hidden fines (fines, public relations, legal fees, and punitive regulatory measures) for a data breach. In addition, business needs to compensate customers, refund any stolen funds, and bear a share value loss. A smart organization will use this opportunity to develop data security and disaster recovery strategies, which entails financial investment. Fines and fees – The PCI Security Standards Council may impose fines or penalties for a data breach. Both regulatory organisations and card network brands will have different fines. Forensic investigations – Major consequences of a data breach include, the business that was attacked will be accountable to perform a forensic investigation to determine the causes of the data breach. These investigations are costly and often yield valuable evidence and insights to prevent future data breaches. Future security costs – Victims of a data breach may have to bear costs of credit monitoring for customers whose data was compromised. This may also include the costs of identity theft repair, card replacement, and additional compliance requirements from the PCI. Reputation Having a good reputation is the most prized asset for any organization. As a business, one must constantly put effort into building and maintaining brand integrity. A single compromising episode like a data breach can trash the best of reputations. According to a PwC report, 85% of customers won't shop at a business if they have concerns about their security policies. Customers value their privacy, and a data breach will be perceived as a lack of regard for their data and privacy. Furthermore, 46% of businesses reported that security breaches harmed their reputation and brand value. Intellectual Property The product blueprints, business strategies, and engineered solutions are some of your most valuable assets for any organization. Your trade secret gives you an added advantage over your competitors. Hence it needs to be protected as some may not hesitate to use breached intellectual property. Other significant consequences of a data breach include; A data breach can pit the CEO against the CISO Poisoned search results on your corporate brand Loss of sales after a data breach Unexpected expenses Less attractive to new employees, especially in tech positions Legal penalties after a data breach Understanding the aftermath of a data breach is an important step to safeguarding your business. The next step is to create an action plan is to protect what you've worked so hard on. How does a Data breach happen? Data breaches sometimes can be traced back to planned attacks. But, on the other hand, it can result from a simple oversight by individuals or flaws in the infrastructure. Accidental Insider For instance, an employee uses a co-worker's computer and reads files without proper approval or permission. However, the access is unintentional/accidental, and no personal information is revealed. The data was breached, however, because it was read by an unauthorised person. Malicious Insider This person deliberately accesses/shares data with the intent of causing harm to an individual or company. The malicious insider may have genuine authorization to use the data, but the intent is to use the info in nefarious ways. Lost or Stolen Devices Any laptop or external hard drive with important information on it that is not encrypted or unlocked goes missing. Malicious Outside Criminals These are hackers who attack several vectors to collect information from a network or an individual. Global cost of data breach According to the Ponemon Institute's Cost of a Data Breach Report, global data breaches cost $3.86 million on average in 2020. The amount in 2020 was somewhat lesser compared to 2019 when it hit $3.92M. The same report found that the average cost of a data breach in 2020 totaled $8.64M. Ways to prevent a data breach Conduct employee security awareness training Control access to data sensibly Update software regularly. Require secure passwords and authentication Simulate phishing attacks Evaluate accounts Limit access to your most valuable data. Review your user account lifecycle processes Insist on complex and unique passwords Protect against authentication bypass Store sensitive personal information securely and protect it during transmission Consider implementing a secure SSO solution Secure all endpoints Segment your network and monitor who's trying to get in and out Manage Vendors - Third-party vendors must comply. Conclusion Protecting against data breaches may appear to be a time-consuming procedure. You will be in a better position if you take an encrusted step to secure your data using various methods, policies, and procedures to ease security threats. FAQ’s How does a data breach impact an organization? Depending upon the company and data type, the consequences may include destruction or corruption of databases, leaking of confidential information, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected. What is the most common data breach? Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting. { "@context": "", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does a data breach impact an organization?", "acceptedAnswer": { "@type": "Answer", "text": "Depending upon the company & data type, the consequences may include destruction or corruption of databases, leaking of confidential info, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected." } },{ "@type": "Question", "name": "What is the most common data breach?", "acceptedAnswer": { "@type": "Answer", "text": "Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting." } }] }

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Stealth-mode startup building a public cloud SaaS data platform.