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

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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.

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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

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India and Big Data Analytics: The Statistics and Facts

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India and Big Data Analytics: The Statistics and Facts

Data science, big data and analytics industry in India is expected to experience 8X growth hitting $16 billion by 2025 from the current $2 billion, experts say. Out of the terrific annual inflow to the analytics industry, nearly 11% can be ascribed to advanced analytics, data science and predictive analytics and a substantial 11% to big data.

In the next seven years, the Indian analytics industry will expand its horizons further and demand more analytics professionals to join the data bandwagon. Separately, the BI and analytics software market revenue in India will touch Rs 1980 crore in 2018, increasing at a rate of 18% per year. As a result, Indian companies and organizations are shifting their focus from traditional data reporting to augmented analytics tools that will not only enhance the process of data preparation and evaluation but will help predict the future outcomes, successfully.

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Trends in Analytics

Several sectors across the Indian industry of companies and startups have started embracing data analytics – no wonder, the data analytics landscape in India is growing rapidly, so is the revenue generation.

Contemporary, architecture-oriented data analytics tools are the order of the day. Rightfully so, the companies and budding startups are replacing tactical and traditional data analytics programs for more strategic approaches. The current breed of fast followers is even seeking hefty investments in advanced analytical solutions powered by AI, ML and Deep Learning. It would lessen the time taken to market and sharpen analytics offerings. Focused data management is bringing forth a rapid shift to the hybrid and cloud data management scenario – through iPaaS (Integration Platform as a Service) tools. Data lakes and hubs are also emerging here and there. They are in demand for ingesting and administering multi-structured data. Nevertheless, a lack of talent pool will cost the industry immensely. It can be a major deterring factor towards their seamless adoption.

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Statistics of Data Analysis

Geographically speaking, more than 64% of revenue generated from data analytics in India comes from the USA. We are a leading exporter of data analytics to the US, taking figures to as high as $1.7 billion. In the FY18 alone, the revenue generation from the US has increased by 45%. Next, ranks the UK with 9.6% revenue generation. Technically, analytics revenue generation in India has almost doubled from last year – in terms of countries Poland, UAE, New Zealand, Belgium, Romania & Spain. Furthermore, Indian analytics firms are not left far behind in the data game – they contribute 4.7% of analytics revenues to Indian analytics market.

Well, it seems India is doing pretty good in terms of adopting cutting data analytics technology and reaping fetching benefits. If interested in data analytics, don’t stay behind. Reach us at DexLab Analytics and throw your queries right away.

 

The blog has been sourced from ― www.dqindia.com/india-analyzes-big-data-science-analytics-market-india

 

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Data Driven Projects: 3 Questions That You Need to Know

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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?

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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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Top 5 Industry Use Cases of Predictive Analytics

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Top 5 Industry Use Cases of Predictive Analytics

Predictive analytics is an effective in-hand tool crafted for data scientists. Thanks to its quick computing and on-point forecasting abilities! Not only data scientists, but also insurance claim analysts, retail managers and healthcare professionals enjoy the perks of predictive analytics modeling – want to know how?

Below, we’ve enumerated a few real-life use cases, existing across industries, threaded with the power of data science and predictive analytics. Ask us, if you have any queries for your next data science project! Our data science courses in Delhi might be of some help.

Customer Retention

Losing customers is awful. For businesses. They have to gain new customers to make up for the loss in revenue. But, it can cost more, winning new customers is usually hailed more costly than retaining older ones.

Predictive analytics is the answer. It can prevent reduction in the customer base. How? By foretelling you the signs of customer dissatisfaction and identifying the customers that are most likely to leave. In this way, you would know how to keep your customers satisfied and content, and control revenue slip offs.

Customer Lifetime Value

Marketing a product is the crux of the matter. Identifying customers willing to spend a large part of their money, consistently for a long period of time is difficult to find. But once cracked, it helps companies optimize their marketing efforts and enhance their customer lifetime value.

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Quality Control

Quality Control is significant. Over time, shoddy quality control measures will affect customer satisfaction ratio, purchasing behavior, thus impacting revenue generation and market share.

Further, low quality control results in more customer support expenses, repairs and warranty challenges and less systematic manufacturing. Predictive analytics help provide insights on potential quality issues, before they turn into crucial company growth hindrances.  

Risk Modeling

Risk can originate from a plethora of source, and it can be any form. Predictive analytics can address critical aspects of risk – it collects a huge number of data points from many organizations and sort through them to determine the potential areas of concern.

What’s more, the trends in the data hint towards unfavorable circumstances that might impact businesses and bottom line in an adverse way. A concoction of these analytics and a sound risk management approach is what companies truly need to quantify the risk challenges and devise a perfect course of action that’s indeed the need of the hour.

Sentiment Analysis

It’s impossible to be everywhere, especially when being online. Similarly, it’s very difficult to oversee everything that’s said about your company.

Nevertheless, if you amalgamate web search and a few crawling tools with customer feedback and posts, you’d be able to develop analytics that’d present you an overview of the organization’s reputation along with its key market demographics and more. Recommendation system helps!

All hail Predictive Analytics! Now, maneuver beyond fuss-free reactive operations and let predictive analytics help you plan for a successful future, evaluating newer areas of business scopes and capabilities.

Interested in data science certification? Look up to the experts at DexLab Analytics.

The blog has been sourced from ― xmpro.com/10-predictive-analytics-use-cases-by-industry

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Explaining the Job Nitty Gritty of a Data Scientist

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Explaining the Job Nitty Gritty of a Data Scientist

What do data scientists do? Since the inception of the term data science, we’ve heard about how it transforms all major sectors, including retail, agriculture, health, legal, telecommunications and automobile industry, but little do we know what exactly the job entails.

Following a recent DataCamp podcast DataFramed, we found out a set of key things about data scientists, and they are as follows:

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Not only tech, but other industries are being explored

A prominent data scientist from Convoy shared insights about how their company is leveraging data science to revolutionize North American trucking industry. Then again, data science is also deemed to make a significant impact on cancer research. So, from this we can understand that data science is not only limited within the walls of technology but has started to seep through different industry verticals.

via GIPHY

It’s beyond AI and self-driving cars

Sure, deep learning and machine learning are powerful applications, but not all data scientists are lost waddling around these top notch techniques. Instead, most of the regular data scientists earn their daily bread and butter through data accumulation and cleaning, creating reports and dashboards, data viz, statistical inference, communicating and convincing decision-makers about key outcomes.

Skill evolution

“Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” – The latter is crucial, so is communicating results.

However, these skills are likely to change very quickly. In a very short span of time. Rapid development across diverse open-source ecosystem is evident; as a result any kind of skill or expertise is unlikely to last long.

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Specialization is the key

It’s better to break down data science into three main components: Business Intelligence, which talks about pulling out data and presenting it to the right people in the form of reports, dashboards and mails; Decision Science, which is all about gathering company data and analyzing it for decision-making; and Machine Learning, which deals with the ways in which we can use data science models and put them into production.

Choosing a distinct career path is an emerging trend and it’s gaining a lot of popularity for all the right reasons.

Ethics is a driving factor

No wonder, this profession is full of uncertainty; at a time, when most of our daily interactions are influenced by algorithms designed by data scientists, what role do you think ethics play? On this context, this is what Omuji Miller, the senior machine learning data scientist at GitHub has to say:

‘We need to have that ethical understanding, we need to have that training, and we need to have something akin to a Hippocratic oath. And we need to actually have proper licenses so that if you actually do something unethical, perhaps you have some kind of penalty, or disbarment, or some kind of recourse, something to say this is not what we want to do as an industry, and then figure out ways to remediate people who go off the rails and do things because people just aren’t trained and they don’t know.’

Soon, we’re approaching a state where the need to maintain ethical standards would come from within data science itself and advocates, legislators and other stakeholders. Hope this consensus comes soon.

The data science revolution is quite the order of the day, and it’s going to stay for a while. So, if you want to ace up your data skills, we’ve superior Data Science Courses in Delhi. Just, visit our website and pore over our course offerings.

 

The blog has been sourced from — hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

 

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Citizen Data Scientists: Who Are They & What Makes Them Special?

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Citizen Data Scientists: Who Are They & What Makes Them Special?

Companies across the globe are focusing their attention on data science to unlock the potentials of their data. But, what remains crucial is finding well trained data scientists for building such advanced systems.

Today, a lot many organizations are seeking citizen data scientists – though the notion isn’t something new, the practice is fairly picking up pace amongst the industries. Say thanks to a number of factors, including perpetual improvement in the quality of tools and difficulty in finding properly skilled data scientists!

Gartner, a top notch analyst firm has been promoting this virgin concept for the past few years. In 2014, the firm predicted that the total number of citizen data scientists would expand 5X faster than normal data scientists through 2017. Although we are not sure if the number forecasted panned out right but what we know is that the proliferating growth of citizen data scientists exceeded our expectations.

Recently, Gartner analyst Carlie Idoine explained a citizen data scientist is one who “creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” They are also termed as “power users”, who’ve the ability to perform cutting edge analytical tasks that require added expertise. “They do not replace the experts, as they do not have the specific, advanced data science expertise to do so. But they certainly bring their OWN expertise and unique skills to the process,” she added.

Of late, citizen data scientists have become critical assets to an organization. They help businesses discover key big data insights and in the process are being asked to derive answers from data that’s not available from regular relational database. Obviously, data can’t be queried through SQL, either. As a result, citizen data scientists are found leveraging machine learning models that end up generating predictions from a large number of data types. No wonder, SQL always sounds effective, but Python statistical libraries and Jupyter notebooks helps you further.

 A majority of industries leverages SQL; it has been data’s lingua franca for years. The sheer knowledge of how to write a SQL query to unravel a quiver of answers out of relational databases still remains a crucial element of company’s data management system as a whole lot of business data of companies are stored in their relational databases. Nevertheless, advanced machine learning tools are widely gaining importance and acceptance.

A wide array of job titles regarding citizen data scientists exists in the real world, and some of them are mutation of business analyst job profile. Depending on an organization’s requirements, the need for experienced analysts and data scientists varies.

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DataRobot, a pioneering proprietary data science and machine learning automation platform developer is recently found helping citizen data scientists through the power of automation. “There’s a lot happening behind the scenes that folks don’t realize necessarily is happening,” Jen Underwood, a BI veteran and the recently hired DataRobot’s director of product marketing said. “When I was doing data science, I would run one algorithm at a time. ‘Ok let’s wait until it ends, see how it does, and try another, one at a time.’ [With DataRobot] a lot of the steps I was taking are now automated, in addition to running the algorithms concurrently and ranking them.”

To everyone’s knowledge, Big Data Analytics is progressing, capabilities that were once restricted within certain domains of professionals are now being accessible by a wider pool of interested parties. So, if you are interested in this new blooming field of opportunities, do take a look at our business analyst training courses in Gurgaon. They would surely help you in charting down a successful analyst career.

 

The blog has been sourced from — datanami.com/2018/08/13/empowering-citizen-data-science

 

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Top 5 Reasons to Feel Excited about Data Analytics This Year

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TOP 5 REASONS TO FEEL EXCITED ABOUT DATA ANALYTICS THIS YEAR

‘Tis the year to be super excited about data analytics! Without further ado, let’s find out why:-

Cloud Infrastructure is Expanding and Fostering Fast-paced Innovations

Considering the recent trends in cloud data and related applications, 2018 is a critical time for cloud analytics. Businesses must steadily transition to a cloud environment and for that a robust and flexible analytics strategy is to be adopted. Through cloud analytics platforms businesses can leverage common data logic and unlock new analytic capabilities to plan, predict, discover, visualize, simulate and manage. In short, what businesses need is a hybrid mode that includes data, analytics and applications spread across multi-cloud and on-premise environments. Research suggests that by employing analytics that are built to work together businesses can increase the total cost of ownership (TCO) by 3-5 times and the return on investment (ROI) can be as high as 171%.

Source: ZDNet

The Power of Machine Learning Unleashed

Machine learning and artificial intelligence have made big progress in the last one year. Hence, automated and AI powered tools are becoming central in decision-making. The rapid growth in automation has profound effect on the way analytics is used. It can be said that machine learning is perking up analytics big time. With the help of automated technologies users can develop contextual insights with ease and uncover patterns from massive volumes of data. And data scientists are harnessing these automated technologies to drive scalable insights for smarter business processes.

Source: Tech Carpenter

The Spreadsheet is Nearing Retirement

The spreadsheet has come a long way since its inception. But, for many businesses it is time to move to better alternatives that are free from some of the inefficiencies and inaccuracies of spreadsheets. For these businesses the solution is shifting to cloud-based models that help connect operational plans to financial plans.

Source: GCN.com

Customer Experience is the Current Competitive Battleground

According to the Harris Interactive study, 88% customers prefer purchasing products or services from a company that offers great customer service over a company that provides the latest innovations. Quality customer experience is crucial for business growth. And for that companies must invest in CEM (customer experience management). CEM technology collects data from varied sources and uses advanced analytics to leverage historical experiences and access data fast. This platform ensures that customers are satisfied, their grievances are addressed and there’s an improvement in sales, profits and brand image.

Source: StoryMiners

Big data Industry to Grow 7 times in 7 years!

Studies suggest that the big data industry in India is likely to become a 20 billion dollar industry by 2015. It is expected that analytics and data science market will grow by 7 times in the next 7 years. Currently, the analytics and big data industry is worth an estimated $2.71 billion in annual revenues and is growing rapidly at a rate of 33.5% CAGR.

Source: Analytics India

Do you know that this year over 16,000 freshers have been hired in the analytics workforce of India? That’s an increase by 33% from last year’s 12,000! Join the big data bandwagon with a professional certificate from this reputed data analyst training institute in Delhi. One of the unique features of this data analyst course in Gurgaon is that it includes trainers who are industry-experts in this field and hence bring with them excellent domain experience.

 

References:

digitalistmag.com/cio-knowledge/2018/01/03/top-10-trends-for-analytics-in-2018-05668659

360logica.com/blog/10-reasons-excited-data-analytics-2018

analyticsindiamag.com/analytics-data-science-industry-in-india-study-2018-by-analytixlabs-aim

getcloudcherry.com/blog/competition-customer-experience

 

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How To Incorporate Embedded Analytics In Your Products or Applications?

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How To Incorporate Embedded Analytics In Your Products or Applications?

The adept R&D team shares a common responsibility – devising incredible products and solutions.  Right from VPs and directors of development to DevOps and system engineers – every professional is well aware about their customer expectations.

In today’s data-driven industry, customers desire to collect information regarding product use, numerous status updates, number of times they engaged with the product and so on. In short, they need access to data that not only help unravel crucial insights but also makes the product fetching.

Definitely, you can create your very own analytics solution, but what if it takes so much time that your competitors outgo you? So, what next?

Embedding analytics into your product or application can be your thing for the day, but how to do it effortlessly?

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Collect Data from Various Sources

We are surrounded by IoT (Internet of Things) and IIoT (Industrial Internet of Things) revolution, where each connected device and sensor accumulates data and this collection and analysis of data is significant to customers. Teams need to sit together and discuss out options for creating an analytics overlay for the product, which will trigger a million questions – how can we get through it? Will our solution scale the growth of number of users? How can we go on improving our products? How do we keep up with all the developments happening in analytics?

Things to Consider While Embedding Analytics

“Start by looking for specific analytic applications that complement your ERP and BI platform investments. In the long term, review vendor capability to support reusable analytics artifacts (i.e., services) in a service-oriented architecture environment,” – says Gartner.

To this, we’ve listed a few functionalities waiting for your attention:

Data Access – How simple do you want your platform to be so as to integrate your data well across all sources and types?

Visualization – Does the platform you chose comprise widgets you need? If not, can you develop them using customization options?

Modeling – How much easier will it be to code for data preparation for user consumption?

Embeddability (iFrame, JS libraries, JavaScript) – Dashboards should be built in a way to suit your customer’s requirements either in mobiles or in web-based applications.

Extensibility (APIs, SDK, JavaScirpt) – No hard fact, for incorporating analytics workflow, solutions supporting API is the key. Otherwise, not getting extensibility will leave you tied to the same analytics platform and can cost you consulting fees and vendor-developed modifications.

Process integration – Generally, integration takes months – so find a vendor who is capable enough to integrate with your products in a week or two so that you remain focused on the benefits alone.

Security – Judge a vendor based on his security credentials – it’s one of the most crucial considerations to tick off your checklist.

As last thoughts, the consideration of these 7 functionalities is just the beginning of embedding analytics into your products or applications. To sail through, the most important thing to do is to choose a suitable vendor who will grow and start thinking of you as a partner and not just any customer. Let him offer you quick, easy and seamless integration, and you solely focus on your customer needs and preferences, and for this, they will LOVE YOU for sure!

If you are still confused about embedded analytics or related concept, let career-building business analyst training courses in Noida help you! For more information on business analyst training delhi, drop by DexLab Analytics.

 

This blog has been sourced from – https://www.sisense.com/blog/going-embedded-pillar-analytics-success

 

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5 Expected Changes You Are Going to Witness Once You Move to SaaS

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Moving to the cloud takes time. One of our friends started with Salesforce in 2009, after 5 years they introduced G Suite (widely known as Google Apps during that time) and it’s now in 2017 that they have adopted a fully cloud-based electronic health record facility. It took 10 years for an organization to resort to a handful number of installed applications for smooth handling of specialized tasks.  

5 Expected Changes You Are Going to Witness Once You Move to SaaS

Nevertheless, their shift to Software-as-a-Service (SaaS) has had an impact on IT spending. Though the expenditure varies from company to company, every organization must have experienced these 5 changes highlighted below:

 

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Better networks

Unsurprisingly, people need and expect faster internet speed these days. Even small businesses have connections that deliver 250Mbps down and 75Mbps (or more) up. An interesting switch is being observed in infrastructure. Today, more or less any medium-organization boasts of 802.11n or 802.11ac WIFI networks, which was unimaginable even few years ago. Deploying wireless mesh devices has become the order of the day now.

Lesser computer upgrades

There was a time, when we used to think that we have to replace our computers every three or five years. In several cases, we had even planned to make a few upgrades to the hardware to keep them running (RAM and hard-drive replacement was a common thought).

But in reality, organizations seldom have to replace parts. In most offices, five year old desktops perform perfectly in delivering the right results. This means definitely days of upgrades are over, all that matters is a faster internet speed and robust app development.

 

More usage of “plug-in and use” systems

More and more companies are seeking so-called “sealed” systems. Though some big companies still go on deploying standardized drive images, but increasingly organizations are found picking off-the-shelf sealed devices, like all-in-one desktops and non-use-configurable laptops.

 

As organizations are moving towards SaaS, Chromebooks are becoming increasingly famous. In fact, more than 20% of the team mentioned in the beginning of the blog uses a Chromebook as their primary work device.

Longer life for devices

Devices, like desktops and laptops that have embraced SaaS seem to have a longer lifecycle. As SaaS mostly depends on browser and network performance, the need for replacing devices has decreased to a great extent. Systems work totally until the device fails to perform or no longer in a position to receive any updates. Also, with SaaS, crucial data don’t remain solely on the device, hence if a system fails, little seems to be lost.

Considerable attention to the peripherals

Peripherals are intimidating. A large number of conventional desktop units have a scanner, printer and copier devices that are supported by a locally installed Windows software or server. Organizations can easily find alternatives of these devices, but it will take some time and effort as well. Few applications and sectors still suffer from minor or significant glitches, but over time, we hope peripherals and accessories will start showing signs of improvements.

 

What changes have you noticed in cloud computing and storage? How do you think the landscape of IT has changed over the past decade?

 

 

To better understand the intricacies of cloud computing and data storage, opt for business analytics course in Delhi from DexLab Analytics. They offer excellent analyst courses in Delhi at really affordable prices. Check out the course itinerary today!

 

The article has been sourced from – https://www.techrepublic.com/article/5-changes-companies-will-see-after-moving-to-saas

 

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