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Secrets behind the Success of AI Implanted Analytics Processes

Posted on June 15, 2018June 15, 2018 by Dexlab

Secrets behind the Success of AI Implanted Analytics Processes

Big data combined with machine learning results in a powerful tool. Businesses are using this combination more and more, with many believing that the age of AI has already begun. Machine learning embedded in analytics processes opens new gateways to success, but companies must be careful about how they use this power. Organizations use this powerful platform in various domains, such as fraud detection, boosting cybersecurity and carrying out personalized marketing campaigns.

Machine learning isn’t a technology that simply speeds up the process of solving existing problems, it holds the potential to provide solutions that weren’t even thought of before; boost innovation and identify problem areas that went unnoticed. To utilize this potent tech the best possible way, companies need to be aware of AI’s strengths as well as limitations. Businesses need to adopt renewed ways of harnessing the power of AI and analytics. Here are the top 4 ways to make the most out of AI and big data.

Context is the key:

Sifting through available information, machine learning can provide insights that are compelling and trustworthy. But, it lacks the ability to judge which results are valuable. For example, taking up a query from a garment store owner, it will provide suggestions based on previous sales and demographic information. However, the store owner might see that some of these suggestions are redundant or impractical. Moreover, humans need to program AI so that it takes into account variables and selects relevant data sets to analyze. Hence, context is the key. Business owners need to present the proper context, based on which AI will provide recommendations.

Broaden your realm of queries:

Machine learning can offer a perfect answer to your query. But, it can do much more. It might stun you by providing appropriate solutions to queries you didn’t even ask. For example, if you are trying to convince a customer to take a particular loan, then machine learning can crunch huge data sets and provide a solution. But is drawing more loans your real goal? Or is the bigger goal increasing revenues? If this is your actual goal, then AI might provide amazing solutions, like opening a new branch, which you probably didn’t even think about. In order to elicit such responses, you must broaden the realm of queries so that it covers different responses.

Have faith in the process:

AI can often figure things out that it wasn’t trained to understand and we might never comprehend how that happened. This is one of the wonders of AI. For example, Google’s neural network was shown YouTube videos for a few days and it learnt to identify cats, something it wasn’t taught.

Such unprecedented outcomes might be welcome for Google, but most businesses want to trust AI, and for that they seek to know how techs arrive at solutions. The insights provided by machine learning are amazing but businesses can act on them only if they trust the tech. It takes time to trust machines, just like it is with humans. In the beginning we might feel the need to verify outputs, but as the algorithms give good results repeatedly, trust comes naturally.

Act sensibly:

Machine learning is a powerful tool that can backfire too. An example of that is the recent misuse of Facebook’s data by Cambridge Analytica, which couldn’t be explained by Facebook authorities too. Companies need to be aware of the consequences of using such an advanced technology. They need to be mindful of how employees use results generated by analytics tools and how third parties handle data that has been shared. All employees don’t need to know that AI is used for inner business processes.

Artificial Intelligence can fuel growth and efficiency for companies, but it takes people to make the best use of it. And how can you take advantage of this data-dominated business world? Enroll for big data Hadoop certification in Gurgaon. As DexLab Analytic’s #BigDataIngestion campaign is ongoing, interested students can enjoy flat 10% discount on big data Hadoop training and data science certifications.

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References: https://www.infoworld.com/article/3272886/artificial-intelligence/big-data-ai-context-trust-and-other-key-secrets-to-success.html

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7-Step Framework to Ensure Big Data Quality

Posted on June 14, 2018May 23, 2020 by Dexlab

7-Step Framework to Ensure Big Data Quality

Ensuring data quality is of paramount importance in today’s data-driven business world because poor quality can render all kinds of data completely useless. Moreover, this data is unreliable and lead to faulty business strategies if analyzed. Data quality is the key to making trustworthy business decisions.

Companies lacking correct data-quality framework are likely to encounter a crisis situation. According to certain reports, big companies are incurring losses of around $9 million/year due to poor data quality. Back in 2013, US Postal Service spent around $1.5 billion in processing mails that were undelivered due to bad data quality.

While the sources of poor quality data can be many, including data entry, data processing and stale data, data in motion is the most vulnerable. The moment data enters the systems of an organization it starts to move. There’s a lot of uncertainty about how to monitor moving data, and the existing processes are fragmented and ad-hoc. Data environments are becoming more and more complex, and the volume, variety and speed of big data can be quite overwhelming.

Here, we have listed some essential steps to ensure that your data is consistently of good quality.

Discover: Systems carrying critical information need to be identified first. For this, source and target system owners must jointly work to discover existing data issues, set quality standards and fix measurement metrics. So, this step ensures that the company has established yardsticks against which data quality of various systems will be measured. However, this isn’t a onetime process, rather it a continuous process that needs to evolve with time.
Define: it is crucial to clearly define the pain points and potential risks associated with poor data quality. Often, some of these definitions might be relevant to only one particular organization, whereas many times these are associated with regulations of the industry/sector the company belongs to.
Assessment: Existing data needs to be assessed against different dimensions, such as accuracy, completeness and consistency of key attributes; timeliness of data, etc. Depending upon the data, qualitative or quantitative assessment might be performed. Existing data policies and their adherence to industry guidelines need to be reviewed.
Measurement Scale: It is important to develop a data measurement scale that can assign numerical values to different attributes. It is better to express definitions using arithmetic values, such as percentages. For example: Instead of categorizing data as good data and bad data, it can be classified as- acceptable data has >95% accuracy.
Design: Robust management processes need to be designed to address risks identified in the previous steps. The data-quality analysis rules need to apply to all the processes. This is especially important for large data sets, where entire data sets need to be analyzed instead of samples, and in such cases the designed solutions must run on Hadoop.
Deploy: Set up appropriate controls, with priority given to the most risky data systems. People executing the controls are as important as the technologies behind them.
Monitor: Once the controls are set up, data quality standards determined in ‘discovery’ phase need to be monitored closely. An automated system is the best for continuous monitoring as it saves both time and money.

Thus, achieving high-quality data requires an all-inclusive platform that continuously monitors data and flags and stops bad data before they can harm business processes. Hadoop is the popular choice for data quality management across the entire enterprise.

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If you are looking for big data Hadoop certification in Gurgaon, visit Dexlab Analytics. We are offering flat 10% discount on our big data Hadoop training courses in Gurgaon. Interested students all over India must visit our website for more details. Our professional guidance will prove highly beneficial for all those wanting to build a career in the field of big data analytics.

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How Big Data Is Influencing HR Analytics for Employees and Employers, Both

Posted on June 13, 2018May 23, 2020 by Dexlab

How Big Data Is Influencing HR Analytics for Employees and Employers, Both

HR analytics powered by big data is aiding talent management and hiring decisions. A Deloitte 2015 report says 35% of companies surveyed revealed that they were actively developing suave data analytics strategies for HR. Moreover, big data analytics isn’t leaving us anytime soon; it’s here to stay for good.

Now, with that coming, employers are of course in an inapt position: whether to use HR analytics or not? And even if they do use the data, how are they going to do that without violating any HR policies/laws or upsetting the employees?

Health Data

While most of the employers are concerned about healthcare and wellness programs for their employees, a whole lot of other employees have started employing HR analytics for evaluation of the program’s effectiveness and addressing the gaps in healthcare coverage with an aim to improve overall program performance.

Today, data is the lifeblood of IT services. Adequate pools of employee data in conjunction with company data are aiding discoveries of the best benefit package for employees where they get best but affordable care. However, in the process, the employers need to be very careful and sensitive to employee privacy at the same time. During data analysis, the process should appear as if the entire organization is involved in it, instead of focusing on a single employee or sub-groups.

Predictive Performance Analytics

For talent management, HR analytics is a saving grace. Especially, owing to its predictive performance. Because of that, more and more employers are deploying this powerful skill to determine future hiring needs and structure a strong powerhouse of talent.

Rightfully so, predictive performance analytics use internal employee data to calculate potential employee turnover, but unfortunately, in some absurd cases, the same data can also be used to influence decisions regarding firing and promotion – and that becomes a problem.

Cutting edge machine learning algorithms dictate whether an event is going to happen or not, instead of what employees are doing or saying. Though it comes with its own advantages, its better when people frame decisions based on data. Because, people are unpredictable and so are the influencing factors.

Burn away irrelevant information

Sometimes, it may happen that employers instead of focusing on the meaningful things end up scrutinizing all the wrong things. For example, HR analytics show that employees living close to the office, geographically, are less likely to leave the office premise early. But, based on this, can we pass off top talent just because they reside a little farther from the office? We can’t, right?!

Hence, the bottom line is, whenever it comes to analyzing data, analysts should always look for the bigger picture rather giving stress on minute features – such as which employee is taking more number of leaves, and so on. Stay ahead of the curve by making the most productive decisions for employees as well as business, as a whole.

In the end, the power of data matters. HR analytics help guide the best decisions, but it’s us who are going to make them. We shouldn’t forget that. Use big data analytics responsibly to prevent any kind of mistrust or legal issues from the side of employees, and deploy them in coordination with employee feedback to come at the best conclusions ever.

Those who are inclined towards big data hadoop certification, we’ve some droolworthy news for you! DexLab Analytics, a prominent data science learning platform has launched a new admission drive: #BigDataIngestion on in-demand skills: data science and big data with exclusive 10% discounts for all students. This summer, unfurl your career’s wings of success with DexLab Analytics!

Get the details here : www.dexlabanalytics.com/events/dexlab-analytics-presents-bigdataingestion

Reference:

The article has been sourced from https://www.entrepreneur.com/article/271753

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Enjoy 10% Discount, As DexLab Analytics Launches #BigDataIngestion

Posted on June 12, 2018June 12, 2018 by Dexlab

This summer, DexLab Analytics, a pioneering analytics training institute in Delhi is back in action with a whole new admission drive for prospective students: #BigDataIngestion with exclusive discount deals on offer. With an aim to promote an intensive data culture, we have launched Summer Industrial Training on Big Data Hadoop/Data Science. An exclusive 10% discount is on offer for all interested candidates. And, the main focus of the admission drive is on Hadoop, Data Science, Machine Learning and Business Analytics certification.

Data analytics is deemed to be the sexiest job of the 21^st century; it’s comes as no surprise that young aspirants are more than eager to grasp the in-demand skills. Especially for them and others, DexLab Analytics emerges as a saving grace. Our state of the art certification training is completely in sync with the vision of providing top-of-the-line quality analytics coaching through fine approaches and student-friendly curriculum.

That being said, #BigDataIngestion is one of its kinds; while Hadoop and Data Science modules are targeted towards B. Tech and B.E students, Data Science and Business Analytics modules are exclusively oriented for Eco, Statistics and Mathematics students. The comprehensive certification courses help students embark on a wishful journey across various big data domains and architectures, triggering high-end IT jobs, but to avail the high-flying discount offer, the students need to present a valid ID card, while enrolling for the courses.

We are glad to announce that already the institute has gathered a good reputation through its cutting edge, open-to-all demo sessions. The demo sessions has helped countless prospective students in understanding the quality of courses and the way they are being imparted. Now, the new offer announced by the team is like an icing on the cake – 10% discount on in-demand big data courses sounds too alluring! And the admission procedure is also as easy as pie; you can either drop by the institute in person, or else can opt for online registration.

In this context, the spokesperson of DexLab Analytics stated, “We are glad to play an active role in the process of development and condoning of data analytics skills amongst the data-friendly students’ community of the country. We go beyond traditional classroom training and provide hands-on industrial training that will enable you to approach your career with confidence”. He further added, “We’ve always been more than overwhelmed to contribute towards the betterment of skilled human resources of the nation, and #BigDataIngestion is no different. It’s a summer industrial training program to equip students with formidable data skills for a brighter future ahead.”

For more information or to register online, click here: DexLab Analytics Presents #BigDataIngestion

#BigDataIngestion: DexLab Analytics Offers Exclusive 10% Discount for Students This Summer

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A Comprehensive Guide on Clustering and Its Different Methods

Posted on June 9, 2018June 9, 2018 by Dexlab

A Comprehensive Guide on Clustering and Its Different Methods

Clustering is used to make sense of large volumes of data, structured or unstructured, by dividing the data into groups. The members of a group are ‘’similar’’ between them and ‘’dissimilar’’ to objects in other groups. The similarity is based on characteristics such as equal distances from a point or people who read the same genre of book. These groups with similar members are called clusters. The various methods of clustering, which we shall be discussing subsequently, help break up data into logical groupings before analyzing the data more deeply.

If a CEO of a company presents a broad question like- ‘’ Help me understand our customers better so that we can improve marketing strategies’’, then the first thing analysts need to do is use clustering methods to the classify customers. Clustering has plenty of application in our daily lives. Some of the domains where clustering is used are:

Marketing: Used to group customers having similar interests or showing identical behavior from large databases of customer data, which contain information on their past buying activities and properties.
Libraries: Used to organize books.
Biology: Used to classify flora and fauna based on their features.
Medical science: Used for the classification of various diseases.
City-planning: identifying and grouping houses based on house type, value and geographical location.
Earthquake studies: clustering existing earthquake epicenters to locate dangerous zones.

Clustering can be performed by various methods, as shown in the diagram below:

Fig 1

The two major techniques used to perform clustering are:

Hierarchical Clustering: Hierarchical clustering seeks to develop a hierarchy of clusters. The two main techniques used for hierarchical clustering are:

Agglomerative: This is a ‘’bottom up’’ approach where first each observation is assigned a cluster of its own, then pairs of clusters are merged as one moves up the hierarchy. The process terminates when only a single cluster is left.
Divisive: This is a ‘’top down’’ approach wherein all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. The process terminates when each observation has been assigned a separate cluster.

Fig 2: Agglomerative clustering follows a bottom-up approach while divisive clustering follows a top-down approach.

Partitional Clustering: In partitional clustering a set of observations is divided into non-overlapping subsets, such that each observation is in exactly one subset. The main partitional clustering method is K-Means Clustering.

The most popular metric used for forming clusters or deciding the closeness of clusters is distance. There are various distance measures. All observations are measured using one particular distance measure and the observation having the minimum distance from a cluster is assigned to it. The different distance measures are:

Euclidean Distance: This is the most common distance measure of all. It is given by the formula:

Distance((x, y), (a, b)) = √(x – a)² + (y – b)²

For example, the Euclidean distance between points (2, -1) and (-2, 2) is found to be

Distance((2, -1), (-2, 2))

Manhattan Distance:

This gives the distance between two points measured along axes at right angles. In a plane with p₁ at (x₁, y₁) and p₂ at (x₂, y₂), Manhattan distance is |x₁ – x₂| + |y₁ – y₂|.

Hamming Distance:

Hamming distance between two vectors is the number of bits we must change to convert one into the other. For example, to find the distance between vectors 01101010 and 11011011, we observe that they differ in 4 places. So, the Hamming distance d(01101010, 11011011) = 4

Minkowski Distance:

The Minkowski distance between two variables X and Y is defined as

The case where p = 1 is equivalent to the Manhattan distance and the case where p = 2 is equivalent to the Euclidean distance.

These distance measures are used to measure the closeness of clusters in hierarchical clustering.

In the next blogs, we will discuss the different methods of clustering in more details, so make sure you follow DexLab Analytics– we provide the best big data Hadoop certification in Gurgaon. Do check our data analyst courses in Gurgaon.

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How Big Data is Revolutionizing Political Campaigns in America

Posted on June 8, 2018 by Dexlab

How Big Data is Revolutionizing Political Campaigns in America

No doubt that big data is altering the manner in which politicians win elections in America, but it is also breaking American politics. So was the verdict in a column by NBC’s Chuck Todd and Carrie Dann.

According to Todd and Dann, recent technological advancements give access to detailed voter information and demographic data, like what they watch, what they shop and what they read; campaign initiators are completely aware of the preferences of voters. Hence, it enables them to target people who are most likely to vote for them through ads and other relevant content. They don’t feel the need to persuade the ones who are less likely to agree with their ideologies. Clearly, this is a crisis situation that fuels polarization within a governing system. It is encouraging campaigns to appeal to their most likely supporters rather than all their constituents. Also, this process is cheaper and faster.

Eitan Hersh, a notable professor of political science at Yale University conducted research on the role of big data and modern technology in mobilizing voters. So, let’s find out if his research work indicates the situation to be as adverse as Todd and Dann claims it to be.

New sources of data:

Earlier, campaigns relied on surveys to generate their data sets, which were based on a sample of the entire population. Now campaigns can use data that is based on the entire population. The data sets that are looked into include voter registration data, plenty of public datasets and consumer databases. Zonal data, like neighborhood income, can be accessed via the Census Bureau. Information about a voter, like her party affiliation, gender, age, race and voting history is often listed in public records. For example, if a democratic campaign is aware that a person has voted for a Democratic party previously, is Latino or of African origin and is under 25 years, then it is highly probable that this person will vote for them.

Once campaigns chalk out their team of supporters, they employ party workers and tools like mails and advertisements to secure their votes.

Hacking the electorate:

According to Eitan Hersh, it is truly impossible to completely understand the interests of the entire population of voters. However, campaigns are focusing heavily on gathering as much data as possible. The process consists of discovering new ways existing data can be utilized to manipulate voters; asking the right questions; predicting the likeliness of a group to vote for a particular candidate, etc. They need to find sophisticated ways to carry out these plans. The ever increasing volume of data is definitely aiding these processes. Campaigns can now customize their targeting based on individual behavior instead of the behavior of a standard constituent.

Types of targeting:

There are chiefly 4 methods of targeting, which are not only used for presidential elections but also for targeting in local elections. These are:

Geographic targeting: This helps target people of a particular zip code, town or city and prevents wastage of money, as ads are focused on people belonging to a specific voting area.
Demographic targeting: This helps targeting ads to specific groups of people, such as professionals working in blue-chip companies, men within ages 45 and 60 and workers whose salaries are within $60k per year for example.
Targeting based on interest: For example, ads can be targeted to people interested in outdoor sports or conservation activities.
Targeting based on behavior: This is basically the process in which past behavior and actions are analyzed and ads are structured based on that. Retargeting is an example of behavioral targeting where ads are targeted to those who have interacted with similar posts in the past.

To conclude, it can be said that victory in politics involves a lot more than using the power of big data to reduce voters to ones (likely voters) and zeros (unlikely voters). Trump’s victory and Clinton’s defeat is an example of this. Although, Clinton targeted voters through sophisticated data-driven campaigns, they might have overlooked hefty vote banks in rural areas.

To read more interesting blogs on big data and its applications, follow Dexlab Analytics – we provide top-quality big data Hadoop certification in Gurgaon. To know more, take a look at our big data Hadoop courses.

References:

https://www.vox.com/conversations/2017/3/16/14935336/big-data-politics-donald-trump-2016-elections-polarization

https://www.entrepreneur.com/article/309356

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Big Data Could Solve Drug Overdose Mini Epidemic

Posted on June 1, 2018June 1, 2018 by Dexlab

Big Data Could Solve Drug Overdose Mini Epidemic

Big data has become an essential part of our everyday living. It’s altering the very ways we collect and process data.

Typically, big data in identifying at-risk groups also shows signs of considerable growth; the reasons being easy availability of data and superior computational power.

The issue of overprescribing of opioids is serious, and over 63000 people has died in the United States last year from drug overdose, out of which more than 75% of deaths occurred due to opioids. Topping that, there are over 2million people in the US alone, diagnosed with opioid use disorder.

But of course, thanks to Big Data: it can help physicians take informed decisions about prescribing opioid to patients by understanding their true characteristics, what makes them vulnerable towards chronic opioid-use disorder. A team from the University of Colorado accentuates how this methodology helps hospitals ascertain which patients incline towards chronic opioid therapy after discharge.

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Big Data offers helps

The researchers at Denver Health Medical Center developed a prediction model based on their electronic medical records to identify which hospitalized patients ran the risk of progressing towards chronic opioid use after are discharged from the hospital. The electronic data in the record aids the team in identifying the number of variables linked to the advancement to COT (Chronic Opioid Therapy); for example, a patient’s history of substance abuse is exposed.

As good news, the model was successful in predicting COT in 79% of patients and no COT in 78% of patients. No wonder, the team claims that their work is a trailblazer for curbing COT risk, and scores better than software like Opioid Risk Tool (ORT), which according to them is not suitable for hospital setting.

Therefore, the prediction model is to be incorporated into electronic health record and activated when a healthcare specialist orders opioid medication. It would help the physician decipher the patient’s risk for developing COT and alter ongoing prescribing practices.

“Our goal is to manage pain in hospitalized patients, but also to better utilize effective non-opioid medications for pain control,” the researchers stated. “Ultimately, we hope to reduce the morbidity and mortality associated with long-term opioid use.”

As parting thoughts, the team thinks it would be relatively cheaper to implement this model and of great support for the doctors are always on the go. What’s more, there are no extra requirements on the part of physicians, as data is already available in the system. However, the team needs to test the cutting edge system a number of times in other health care platforms to determine if it works for a diverse range of patient populations.

On that note, we would like to say DexLab Analytics offers SAS certification for predictive modeling. We understand how important the concept of predictive analytics has become, and accordingly we have curated our course itinerary.

The blog has first appeared on – https://dzone.com/articles/using-big-data-to-reduce-drug-overdoses

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10 Key Areas to Focus When Settling For an Alternative Data Vendor

Posted on May 8, 2018May 23, 2020 by Dexlab

Unstructured data is the new talk of the town! More than 80% of the world’s data is in this form, and big wigs of financial world need to confront the challenges of administering such volumes of unstructured data through in-house data consultants.

FYI, deriving insights from unstructured data is an extremely tiresome and expensive process. Most buy-sides don’t have access to these types of data, hence big data vendors are the only resort. They are the ones who transform unstructured content into tradable market data.

Here, we’ve narrowed down 10 key areas to focus while seeking an alternative data vendor.

Structured data

Banks and hedge funds should seek alternative data vendors that can efficiently process unstructured data into 100% machine readable structured format – irrespective of data form.

Derive a fuller history

Most of the alternative data providers are new kid in the block, thus have no formidable base of storing data. This makes accurate back-testing difficult.

Data debacles

The science of alternative data is punctured with a lot of loopholes. Sometimes, the vendor fails to store data at the time of generation – and that becomes an issue. Transparency is very crucial to deal with data integrity issues so as to nudge consumers to come at informed conclusions about which part of data to use and not to use.

Context is crucial

While you look at unstructured content, like text, the NLP or natural language processing engine must be used to decode financial terminologies. As a result, vendors should create their own dictionary for industry related definitions.

Version control

Each day, technology gets better or the production processes change; hence vendors must practice version control on their processes. Otherwise, future results will be surely different from back-testing performance.

Point-in-time sensitivity

This generally means that your analysis includes data that is downright relevant and available at particular periods of time. In other cases, there exists a higher chance for advance bias being added in your results.

Relate data to tradable securities

Most of the alternative data don’t include financial securities in its scope. The users need to figure out how to relate this information with a tradable security, such as bonds and stocks.

Innovative and competitive

AI and alternative data analytics are dramatically changing. A lot of competition between companies urges them to stay up-to-date and innovative. In order to do so, some data vendors have pooled in a dedicated team of data scientists.

Data has to be legal

It’s very important for both vendors and clients to know from where data is coming, and what exactly is its source to ensure it don’t violate any laws.

Research matters

Few vendors have very less or no research establishing the value of their data. In consequence, the vendor ends up burdening the customer to carry out early stage research from their part.

In a nutshell, alternative data in finance refer to data sets that are obtained to inject insight into the investment process. Most hedge fund managers and deft investment professionals employ these data to derive timely insights fueling investment opportunities.

Big data is a major chunk of alternative data sets. Now, if you want to arm yourself with a good big data hadoop certification in Gurgaon then walk into DexLab Analytics. They are the best analytics training institute in India.

The article has been sourced from – http://dataconomy.com/2018/03/ten-tips-for-avoiding-an-alternative-data-hangover

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How Data Exhaust is Leveraged for Your Business

Posted on May 4, 2018May 4, 2018 by Dexlab

Big data is the KING of corporate kingdom. Every company is somehow using this vital tech tool; even if they are not using it, they are thinking of it.

A 2017 survey says, around 53% of companies were relying on big data for their business operations. Each company focuses on a particular variant of data. Some of the data types are considered most important, while others are left out. Now what happens to the data that is kept aside?

Data exhaust can be a valuable addition for a company – if leveraged properly.

Explaining Data Exhaust

It entirely deals with the data that is leftover but produced by the company itself. Keep in mind, when you try collect information from a specific set of data, a whole lot of information is also collected at the same time. So, many organizations might be sitting on a gold mine of data but without acknowledging the importance of that data. In instances like this, data exhaust can be very helpful across numerous business development channels.

Market Research

The best way to use data exhaust is through extensive market research. Know your audience is the key. Customers are crucial for effective marketing and product development. Nevertheless, the former involves manual research as well as analytical research, which once again leads us to analytics.

Through data exhaust, you get to know everything your customers do on your website – thus, can understand what they like better.

Cyber Security

As a potent threat, cyber crime results into potential costs to businesses all across the world. So, what role does data exhaust play? At best, it can help determine risk across different databases to develop superior cyber security plan.

Product Development

Importantly, businesses work on a plethora of projects at the same time. As a result, the issue of time crunch pops up. No one can do everything all at once, and data exhaust helps in sharpening whatever is important. Like, if your excess data says that most of your viewers visit your site through mobile device, it’s better to develop a mobile app to serve the customers better.

All Data Is Not Important

All data is not useful. Though data exhaust is useful, yet there would be times when you will come across bad data. You need to shed off those data, and get rid of data of that manner that is meaningless. Ask data experts which data to keep and which is irrelevant. Data that is of no use needs to be destroyed, because a company cannot keep trash for long.

Be Responsible for Data

Its clear data exhaust is all good and great for business, but it’s always suggestible to be cautious and responsible. There can be many legal implications, hence its suggestible to consult a data professional who have the desired know-how, otherwise things can get a bit complicated.

In this world of competitive technology, businesses have to be very careful about how they are using data to avoid any kind of negative outcomes. Be responsible and use data correctly; big data help frame a highly effective business strategy.

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The blog is sourced from – http://dataconomy.com/2018/03/how-data-exhaust-can-be-leveraged-to-benefit-your-company

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