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Top 4 Python Industrial Use-Cases: Explained

Posted on April 16, 2019March 14, 2020 by Dexlab

Top 4 Python Industrial Use-Cases: Explained

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Python is one of the fastest-growing and most popular coding languages in the world; a large number of developers use it on daily basis and why not, it works brilliantly for a plethora of developer job roles and data science positions – starting from scripting solution for sysadmins to supporting machine learning algorithms to fueling web development, Python can work wonders across myriad platforms!

Below, we’ve rounded up 4 amazing Python industrial use-cases; scroll ahead:

Insurance

Widely used in generating business insights; courtesy machine learning.

Case Study:

Smaller firms driven by machine learning gave stiff competition to a US multinational finance and insurance corporation. In return, the insurer formed teams and devised a new set of services and applications based on ML algorithms to enjoy a competitive edge. However, the challenge was that with so many data science tools, numerous versions of Python came into the picture and gave rise to compatibility issues. As a result, the company finalized only one version of Python, which was then used in line with machine learning algorithms and tools to derive specific results.

Finance

Data mining helps determine cross-sell opportunities.

Case Study:

Another US MNC dealing in financial services showed interest in mining complex customer behavioral data. Using Python, the company launched a series of ML and data science initiatives to dig into its structured data that it has been gathering for years and correlated it with an army of unstructured data, gathered from social media and web to enhance cross-selling and retrieve resources.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Case Study:

Recently, the International Space Station struck a deal with an American MNC dealing in military, defense and aerospace technology; the latter has been asked to provide a series of systems to the ISS. The critical safety systems were mostly written in languages, like Ada; they didn’t fare well in terms of scripting tasks, data science analysis or GUI creation. That’s why Python was chosen; it offered bigger contract value and minimum exposure.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Case Study:

A top-notch US department store chain equipped with an in-store banking division gathered data and stored it in a warehouse. The main aim of the company was to share the information with multiple platforms to fulfill its supply chain, analytics, retail banking and reporting needs. Though the company chose Python for on-point data manipulation, each division came up with their own versions of Python, resulting in a new array of issues. In the end, the company decided to keep a standard Python; this initiative not only resulted in amplifying engineering speed but also reduced support costs.

As end notes, Python is the next go-to language and is growing each day. If you have dreams of becoming an aspiring programmer, you need to book the best Python Certification Training in Delhi. DexLab Analytics is a premier Python training institute in Delhi; besides Python, it offers in-demand skill development courses for interested candidates.

The blog has been sourced from— www.techrepublic.com/article/python-5-use-cases-for-programmers

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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

Posted on February 8, 2019February 8, 2019 by Dexlab

Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

Programming skills are indispensable for data science professionals. The main job of machine learning engineers and data scientists is drawing insights from data, and their expertise in programming languages enable them to do this crucial task properly. Research has shown that professionals of the data science field typically work with three languages simultaneously. So, which ones are the most popular? Are some languages more likely to be used together?

Recent studies explain that certain programming languages are used jointly besides other programming languages that are used independently. With the survey data collected from Kaggle’s 2018 Machine Learning and Data Science study, usage patterns of over 18,000 data science experts working with 16 programming languages were analyzed. The research revealed that these languages can actually be categorized into smaller sets, resulting in 5 main groupings. The nature of the groupings is indicative of specific roles or applications that individual groups support, like analytics, front-end work and general-purpose tasks.

Principal Component Analysis for Dimension Reduction

In this article, we will explain how Bob E. Hayes, PhD holder, scientist, blogger and data science writer has used principal component analysis, a type of data reduction method, to categorize 16 different programming languages. Herein, the relationship among various languages is inspected before putting them in particular groups. Basically, principal component analysis looks into statistical associations like covariance within a large collection of variables, and then justifies these correlations with the help of a few variables, called components.

Principal component matrix presents the results of this analysis. The matrix is an nXm table, where:

n= total no. of original variables, which in this case are the number of programming languages

m= number of main components

The strength of relationship between each language and underlying components is represented by the elements of the matrix. Overall, the principal component analysis of programming language usage gives us two important insights:

How many underlying components (groupings of programming languages) describe the preliminary set of languages
The languages that go best with each programming language grouping

Result of Principal Component Analysis:

The nature of this analysis is exploratory, meaning no pre-defined structure was imposed on the data. The result was primarily driven by the type of relationship shared by the 16 languages. The aim was to explain the relationships with as less components as possible. In addition, few rules of thumb were used to establish the number of components. One was to find the number of eigen values with value greater than 1 – that number determines the number of components. Another method is to identify the breaking point in the scree plot, which is a plot of the 16 eigen values.

businessoverbroadway.com

5-factor solution was chosen to describe the relationships. This is owing to two reasons – firstly, 5 eigen values were greater than one and secondly, the scree plot showed a breaking point around 6^th eigen value.

Following are two key interpretations from the principal component matrix:

Values greater than equal to .45 have been made bold
The headings of different components are named on the basis of tools that loaded highly on that component. For example, component 4 has been labeled as Python, Bash, Scala because these languages loaded highest on this component, implying respondents are likely to use Bash and Scala if they work with Python. Other 4 components were labeled in a similar manner.

Groupings of Programming Languages

The given data set is appropriately described by 5 tool grouping. Below are given 5 groupings, including the particular languages that fall within the group, meaning they are likely to be used together.

Java, Javascript/Typescript, C#/.NET, PHP
R, SQL, Visual Basic/VBA, SAS/STATA
C/C++, MATLAB
Python, Bash, Scala
Julia, Go, Ruby

One programming language didn’t properly load into any of the components: SQL. However, SQL is used moderately with three programming languages, namely Java (component 1), R (component 2) and Python (component 4).

It is further understood that the groupings are determined by the functionality of different languages in the group. General-purpose programming languages, Python, Scala and Bash, got grouped under a single component, whereas languages used for analytical studies, like R and the other languages under comp. 2, got grouped together. Web applications and front-end work are supported by Java and other tools under component 1.

Conclusion:

Data science enthusiasts can succeed better in their projects and boost their chances of landing specific jobs by choosing correct languages that are suited for the job role they want. Being skilled in a single programming language doesn’t cut it in today’s competitive industry. Seasoned data professionals use a set of languages for their projects. Hence, the result of the principal component analysis implies that it’s wise for data pros to skill up in a few related programming languages rather than a single language, and focus on a specific part of data science.

For more help with your data science learning, get in touch with DexLab Analytics, a leading data analyst training institute in Delhi. Also check our Machine learning courses in Delhi to be trained in the essential and latest skills in the field.

Reference: http://customerthink.com/usage-driven-groupings-of-data-science-and-machine-learning-programming-languages

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General Python Guide 2019: Learning Data Analytics with Python

Posted on February 6, 2019May 23, 2020 by Dexlab

General Python Guide 2019: Learning Data Analytics with Python

Python and data analytics are possibly three of the most commonly heard words these days. In today’s burgeoning tech scene, being skillful in these two subjects can prove very profitable. Over the years, we have seen the importance of Python education in the field of data science skyrocketing.

So here we present a general guide to help start off your Python learning:

Reasons to Choose Python:

Popularity

With over 40% data scientists preferring Python, it is clearly one of the most widely used tools in data analysis. It has risen in popularity above SAS and SQL, only lagging behind R.

General Purpose Language

There might be many other great tools in the market for analyzing data, like SAS and R, but Python is the only trustworthy general-purpose language valid across a number of application domains.

Step 1: Setup Python Environment

Setting up Python environment is uncomplicated, but a primary step. Downloading the free Anaconda Python package is recommended. Besides core Python language, it includes all the essential libraries, such as Pandas, SciPy, NumPy and IPython, and graphical installer also. Post installation, a package containing several programs is launched, most important one being iPython also known as Jupyter notebook. After launching the notebook, the terminal opens and a notebook is started in the browser. This browser works as the coding platform and there’s no need for internet connection even.

Step 2: Knowing Python Fundamentals

Getting familiar with the basics of Python can happen online. Active participation in free online courses, where video tutorials, practice exercises are plentiful, can help you grasp the fundamentals quickly. However, if you are seeking expert guidance, you must explore our Python data science courses.

Step 3: Know Key Python Packages used for Data Analysis

Since it is a general purpose language, Python’s utility stretches beyond data science. But there are plentiful Python libraries useful in data functionalities.

Numpy – essential for scientific computing

Matplotib – handy for visualization and plotting

Pandas – used in data operations

Skikit-learn – library meant to help with data mining and machine learning activities

StatsModels – applied for statistical analysis and modeling

Scipy-SciPy – the Numpy extension of Python; it is a set of math functions and algorithms

Theano – package defining multi-dimensional arrays.

Step 4: Load Sample Data for Practice

Working with sample datasets is a great way of getting familiar with a programming language. Through this kind of practice, candidates can try out different methods, apply novel techniques and also pinpoint areas of strength and in need of improvement.

Python library StatModels contains preloaded datasets for practice. Users can also download dataset from CSV files or other sources on web.

Step 5: Data Operations

Data administration is a key skill that helps extract information from raw data. Majority of times, we get access to crude data that cannot be analyzed straightaway; it needs to be manipulated before analyzing. Python has several tools for formatting, manipulating and cleaning data before it is examined.

Step 6: Efficient Data Visualization

Visuals are very valuable for investigative data analysis and also explaining results lucidly. The common Python library used for visualization is Matplotlib.

Step 7: Data Analytics

Formatting data and designing graphs and plots are important in data analysis. But the foundation of analytics is in statistical modeling, data mining and machine learning algorithms. Having libraries like StatsModels and Scikit-learn, Python provides all necessary tools essential for performing core analyzing functions.

Concluding

As mentioned before, the key to learning data analytics with Python is practicing with imported data sets. So without delay, start experimenting with old operations and new techniques on data sets.

For more useful blogs on data science, follow DexLab Analytics – we help you stay updated with all the latest happenings in the data world! Also, check our excellent Python courses in Delhi NCR.

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Being a Statistician Matters More, Here’s Why

Posted on January 24, 2019May 23, 2020 by Dexlab

Being a Statistician Matters More, Here’s Why

Right data for the right analytics is the crux of the matter. Every data analyst looks for the right data set to bring value to his analytics journey. The best way to understand which data to pick is fact-finding and that is possible through data visualization, basic statistics and other techniques related to statistics and machine learning – and this is exactly where the role of statisticians comes into play. The skill and expertise of statisticians are of higher importance.

Below, we have mentioned the 3R’s that boosts the performance of statisticians:

Recognize – Data classification is performed using inferential statistics, descriptive and diverse other sampling techniques.

Ratify – It’s very important to approve your thought process and steer clear from acting on assumptions. To be a fine statistician, you should always indulge in consultations with business stakeholders and draw insights from them. Incorrect data decisions take its toll.

Reinforce – Remember, whenever you assess your data, there will be plenty of things to learn; at each level, you might discover a new approach to an existing problem. The key is to reinforce: consider learning something new and reinforcing it back to the data processing lifecycle sometime later. This kind of approach ensures transparency, fluency and builds a sustainable end-result.

Now, we will talk about the best statistical techniques that need to be applied for better data acknowledgment. This is to say the key to becoming a data analyst is through excelling the nuances of statistics and that is only possible when you possess the skills and expertise – and for that, we are here with some quick measures:

Distribution provides a quick classification view of values within a respective data set and helps us determine an outlier.

Central tendency is used to identify the correlation of each observation against a proposed central value. Mean, Median and Mode are top 3 means of finding that central value.

Dispersion is mostly measured through standard deviation because it offers the best scaled-down view of all the deviations, thus highly recommended.

Understanding and evaluating the data spread is the only way to determine the correlation and draw a conclusion out of the data. You would find different aspects to it when distributed into three equal sections, namely Quartile 1, Quartile 2 and Quartile 3, respectively. The difference between Q1 and Q3 is termed as the interquartile range.

While drawing a conclusion, we would like to say the nature of data holds crucial significance. It decides the course of your outcome. That’s why we suggest you gather and play with your data as long as you like for its going to influence the entire process of decision-making.

On that note, we hope the article has helped you understand the thumb-rule of becoming a good statistician and how you can improve your way of data selection. After all, data selection is the first stepping stone behind designing all machine learning models and solutions.

Saying that, if you are interested in learning machine learning course in Gurgaon, please check out DexLab Analytics. It is a premier data analyst training institute in the heart of Delhi offering state-of-the-art courses.

The blog has been sourced from — www.analyticsindiamag.com/are-you-a-better-statistician-than-a-data-analyst

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Discover Top 5 Data Scientist Archetypes

Posted on January 4, 2019September 24, 2020 by Dexlab

Discover Top 5 Data Scientist Archetypes

Data science jobs are labelled as the hottest job of the 21^st century. For the last few years, this job profile is indeed gaining accolades. And yes, that’s a good thing! Although much has been said about how to progress towards a successful career as a data scientist, little do we know about the types of data scientists you may come across in the industry! In this blog, we are going to explore the various kinds of data scientists or simply put – the data scientist archetypes found in every organization.

Generalist

This is the most common type of data scientists you find in every industry. The Generalist contains an exemplary mixture of skill and expertise in data modelling, technical engineering, data analysis and mechanics. These data scientists interact with researchers and experts in the team. They are the ones who climb up to the Tier-1 leadership teams, and we aren’t complaining!

Detective

He is the one who is prudent and puts enough emphasis on data analysis. This breed of data scientists knows how to play with the right data, incur insights and derive conclusions. The researchers say, with an absolute focus on analysis, a detective is familiar with numerous engineering and modelling techniques and methods.

Maker

The crop of data scientists who are obsessed with data engineering and architecture are known as the Makers. They know how to transform a petty idea into concrete machinery. The core attribute of a Maker is his knowledge in modelling and data mechanisms, and that’s what makes the project reach heights of success in relatively lesser time.

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Oracle

Having mastered the art and science of machine learning, the Oracle data scientist is rich in experience and full of expertise. Tackling the meat of the problem cracks the deal. Also called as data ninjas, these data scientists possess the right know how of how to deal with specific tools and techniques of analysis and solve crucial challenges. Elaborate experience in data modelling and engineering helps!

Unicorn

The one who runs the entire data science team and is the leader of the team is the Unicorn. A Unicorn data scientist is reckoned to be a data ninja or an expert in all aspects of data science domain and stays a toe ahead to nurture all the data science nuances and concepts. The term is basically a fusion version of all the archetypes mentioned above weaved together – the job responsibility of a data unicorn is impossible to suffice, but it’s a long road, peppered with various archetypes as a waypoint.

Organizations across the globe, including media, telecom, banking and financial institutions, market research companies, etc. are generating data of various types. These large volumes of data call for impeccable data analysis. For that, we have these data science experts – they are well-equipped with desirable data science skills and are in high demand throughout industry verticals.

Thinking of becoming a data ninja? Try data science courses in Delhi NCR: they are encompassing, on-point and industry-relevant.

The blog has been sourced from ― www.analyticsindiamag.com/see-the-6-data-scientist-archetypes-you-will-find-in-every-organisation

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Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

Posted on December 10, 2018December 10, 2018 by Dexlab

Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

Are you looking for a data analyst job? The chances of bagging a job at a private bank are more than that a public bank. The former is more likely to hire you than the latter.

As a matter of fact, data analytics is widely being used in the private banking and e-commerce sectors – according to a report on the state of data analytics in Indian business. The veritable report was released last month by Analytics India Magazine in association with the data science institute INSOFE. Next to banking and ecommerce, telecom and financial service sectors have started to adopt the tools of data analytics on a larger scale, the report mentioned.

The report was prepared focusing on 50 large firms across myriad sectors, namely Maruti Suzuki and Tata Motors in automobiles, ONGC and Reliance Industries under oil-drilling and refineries, Zomato and Paytm under e-commerce tab, and HDFC and the State Bank of India in banking.

If you follow the study closely, you will discover that in a nutshell, data analytics and data science boasts of a healthy adoption rate all throughout – 64% large Indian firms has started implementing this wonder tool at their workplaces. As a fact, if a firm is found to have an analytics penetration rate of minimum 0.75% (which means, at least one analytics professional is found out of 133 employees in a company), we can say the company has adopted analytics.

Nevertheless, the rate of adoption was not universal overall. We can see that infrastructure firms have zero adoption rates – this might be due to a lack of resources to power up a robust analytics facility or whatever. Also, steel, power and oil exhibited low adoption rates as well with not even 40% of the surveyed firms crossing the 0.75% bar. On contrary, private banks and telecom industry showed a total 100% adoption rates.

Astonishingly, public sector banks showed a 50% adoption rate- almost half of the rate in the private sector.

The study revealed more and more companies in India are looking forward to data analytics to boost sales and marketing initiatives. The tools of analytics are largely employed in the sales domain, followed by finance and operations.

Apparently, not much of the results were directly comparable with that of the last year’s study. Interestingly, one metric – analytics penetration rate – was measured last year as well, which is nothing but the ratio of analytics-oriented employees to the total. Also, last year, you would have found one out of 59 employees in an average organization, which has now reached one data analyst for every 36 employees.

For detailed information, read the full blog here: qz.com/india/1482919/banks-telcos-e-commerce-firms-hire-most-data-analysts-in-india

If you are interested in following more such interesting blogs and technology-related updates, follow DexLab Analytics, a premium analytics training institute headquartered in Gurgaon, Delhi. Grab a data analyst certification today and join the bandwagon of success.

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Data Driven Projects: 3 Questions That You Need to Know

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

Data Driven Projects: 3 Questions That You Need to Know

Today, data is an asset. It’s a prized possession for companies – it helps derive crucial insights about customers, thus future business operations. It also boosts sales, predicts product development and optimizes delivery chains.

Nevertheless, several recent reports suggest that even though data floats around in abundance, a bulk of data-driven projects fail. In 2017 alone, Gartner highlighted 60% of big data projects fail – so what leads it? Why the availability of data still can’t ensure success of these projects?

Right data, do I have it?

It’s best to assume the data which you have is accurate. After all, organizations have been keeping data for years, and now it’s about time they start making sense out of it. The challenge that they come across is that this data might give crucial insights about past operations, but for present scenario, they might not be good enough.

To predict the future outcomes, you need fresh, real-time data. But do you know how to find it? This question leads us to the next sub-head.

Where to find relevant data?

Each and every company does have a database. In fact, many companies have built in data warehouses, which can be transformed into data lakes. With such vast data storehouses, finding data is no more a difficult task, or is it?

Gartner report shared, “Many of these companies have built these data lakes and stored a lot of data in them. But if you ask the companies how successful are you doing predictions on the data lake, you’re going to find lots and lots of struggle they’re having.”

Put simply, too many data storehouses may pose a challenge at times. The approach, ‘one destination for all data in the enterprise’ can be detrimental. Therefore, it’s necessary to look for data outside the data warehouses; third party sources can be helpful or even company’s partner network.

How to combine data together?

Siloed data can be calamitous. Unsurprisingly, data is available in all shapes and is derived from numerous sources – software applications, mobile phones, IoT sensors, social media platforms and lot more – compiling all the data sources and reconciling data to derive meaningful insights can thus be extremely difficult.

However, the problem isn’t about the lack of technology. A wide array of tools and software applications are available in the market that can speed up the process of data integration. The real challenge lies in understanding the crucial role of data integration. After all, funding an AI project is no big deal – but securing a budget to address the problem of data integration efficiently is a real challenge.

In a nutshell, however data sounds all promising, many organizations still don’t know how achieve full potential out of data analytics. They need to strengthen their data foundation, and make sure the data that is collected is accurate and pulled out from a relevant source.

A good data analyst course in Gurgaon can be of help! Several data analytics training institutes offer such in-demand skill training course, DexLab Analytics is one of them. For more information, visit their official site.

The blog has been sourced from ― dataconomy.com/2018/10/three-questions-you-need-to-answer-to-succeed-in-data-driven-projects

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5 Incredible Techniques to Lift Data Analysis to the Next Level

Posted on October 22, 2018October 24, 2018 by Dexlab

5 Incredible Techniques to Lift Data Analysis to the Next Level

Today, it’s all about converting data into actionable insights. How much data an organization collects from a plethora of sources is all companies cares of. To understand the intricacies of the business operations and helps team identify future trends, data is the power.

Interestingly, there’s more than one way to analyze data. Depending on your requirement and types of data you need to have, the perfect tool for data analytics will fluctuate. Here, we’ve 5 methods of data analysis that will help you develop more relevant and actionable insights.

DexLab Analytics is a premier data analytics training institute in Noida. It offers cutting edge data analyst courses for data enthusiasts.

Difference between Quantitative and Qualitative Data:

What type of data do you have? Quantitative or qualitative? From the name itself you can guess quantitative deal is all about numbers and quantities. The data includes sales numbers, marketing data, including payroll data, revenues and click-through rates, and any form of data that can be counted objectively.

Qualitative data is relatively difficult to pin down; they tend to be more subjective and explanatory. Customer surveys, interview results of employees and data that are more inclined towards quality than quantity are some of the best examples of qualitative data. As a result, the method of analysis is less structured and simple as compared to quantitative techniques.

Measuring Techniques for Quantitative Data:

Regression Analysis

When it comes to making forecasts and predictions and future trend analysis, regression studies are the best bet. The tool of regression measures the relationship between a dependent variable and an independent variable.

Hypothesis Testing

Widely known as ‘T Testing’, this type of analytics method boosts easy comparison of data against the hypothesis and assumptions you’ve made regarding a set of operations. It also allows you to forecast future decisions that might affect your organization.

Monte Carlo Simulation

Touted as one of the most popular techniques to determine the impact of unpredictable variables on a particular factor, Monte Carlo simulations implement probability modeling for smooth prediction of risk and uncertainty. This type of simulation uses random numbers and data to exhibit a series of possible outcomes for any circumstance based on any results. Finance, engineering, logistics and project management are a few industries where this incredible tool is widely used.

Measuring Techniques for Qualitative Data:

Unlike quantitative data, qualitative data analysis calls for more subjective approaches, away from pure statistical analysis and methodologies. Though, you still will be able to extract meaningful information from data by employing different data analysis techniques, subject to your demands.

Here, we’ve two such techniques that focus on qualitative data:

Content Analysis

It works best when working with data, like interview data, user feedback, survey results and more – content analysis is all about deciphering overall themes emerging out of a qualitative data. It helps in parsing textual data to discover common threads focusing on improvement.

Narrative Analysis

Narrative analysis help you understand organizational culture by the way ideas and narratives are communicated within an organization. It works best when planning new marketing campaigns and mulling over changes within corporate culture – it includes what customers think about an organization, how employees feel about their job remuneration and how business operations are perceived.

Agreed or not, there’s no gold standard for data analysis or the best way to perform it. You have to select the method, which you deem fit for your data and requirements, and unravel improved insights and optimize organizational goals.

The blog has been sourced from — www.sisense.com/blog/5-techniques-take-data-analysis-another-level

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How Data Analytics Should Be Managed In Your Company, and Who Will Lead It?

Posted on October 11, 2018May 23, 2020 by Dexlab

How Data Analytics Should Be Managed In Your Company, and Who Will Lead It?

In the last couple of years, data management strategies have revolutionized a lot. Previously, the data management used to come under the purview of the IT department, while data analytics was performed based on business requirements. Today, a more centralized approach is being taken uniting the roles of data management and analytics – thanks to the growing prowess of predictive analytics!

Predictive analytics has brought in a significant change – it leverages data and extracts insights to enhance revenue and customer retention. However, many companies are yet to realize the power of predictive analytics. Unfortunately, data is still siloed in IT, and several departments still depend on basic calculations done by Excel.

But, of course, on a positive note, companies are shifting focus and trying to recognize the budding, robust technology. They are adopting predictive analytics and trying to leverage big data analytics. For that, they are appointing skilled data scientists, who possess the required know-how of statistical techniques and are strong on numbers.

Strategizing Analytical Campaigns

An enterprise-wide strategy is the key to accomplish analytical goals and how. Remember, the strategy should be encompassing and incorporate needful laws that need to be followed, like GDPR. This signifies effective data analytics strategies begin from the top.

C-suite is a priority for any company, especially which looks forward to defining data and analytics, but each company also require a designated person, who would act as a link between C-suite and the rest of the company. This is the best way to mitigate the wrong decisions and ineffective strategies that are made in silos within the organization.

Chief Data Officers, Chief Analytics Officers and Chief Technology Officers are some of the most popular new age job designations that have come up. Eminent personalities in these fetching positions play influential roles in strategizing and executing a successful corporate-level data analytics plan. The main objective of them is to provide analytical support to the business units, determine the impact of analytical strategies and ascertain and implement innovative analytical prospects.

Defensive Vs Offensive Data Strategy

To begin, defensive strategy deals with compliance with regulations, prevention of theft and fraud detection, while offensive strategy is about supporting business achievements and strategizing ways to enhance profitability, customer retention and revenue generation.

Generally, companies following a defensive data strategy operate across industries that are heavily regulated (for example, pharmaceuticals, automobile, etc.) – no doubt, they need more control on data. Thus, a well-devised data strategy has to ensure complete data security, optimize the process of data extraction and observe regulatory compliance.

On the other hand, offensive strategy requires more tactical implementation of data. Why? Because they perform in a more customer-oriented industry. Here, the analytics have to be more real-time and their numerical value will depend on how quickly they can arrive at decisions. Hence, it becomes a priority to equip the business units with analytical tools along with data. As a result, self-service BI tools turns out to be a fair deal. They are found useful. Some of the most common self-service BI vendors are Tableau and PowerBI. They are very easy to use and deliver the promises of flexibility, efficacy and user value.

As final remarks, the sole responsibility of managing data analytics within an organization rests on a skilled team of software engineers, data analysts and data scientists. Only together, they would be able to take the charge of building successful analytical campaigns and secure the future of the company.

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The blog has been sourced from — dataconomy.com/2018/09/who-should-own-data-analytics-in-your-company-and-why

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