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3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

Posted on September 19, 2018May 23, 2020 by Dexlab

3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

The job responsibility of data scientists is no mean feat. They stay under a lot of pressure. A wide number of stumbling blocks are laid in front of them, which makes it really difficult for them to secure the long-shot business goals and objectives.

As prevention is better than cure – being aware of the challenges always help data scientists plot the shortest and smartest route to success, and we can’t agree more. Brace yourselves! Below, we’ve enumerated some of the challenges data scientists face while getting started with an IoT project:

Inferior Data Quality

Messy data is life and soul of data scientists. Irrespective of business scale, the job of every data scientist is to organize data in the correct manner. But, however organizing them may require adequate time as well as hard work.

A fundamental rule – avoid manual data, wherever possible. Intelligent data compilation is the final key to high quality data, which is a prerequisite for favorable company operation. It includes crisp communication, regular anomaly detection, logic determination and well-defined industry standards. Another way to tame your data can be through application integration tools – they are a fabulous way to automate data entry and lessen escalation of typographical errors, individual eccentricities, staggering spellings and more from the data.

Once data is in the right format and quality, data scientists can start slicing off the data they don’t need any more, which takes us to the next step.

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Shedding Out Excessive Data

Though big data is found in abundance, too much of data can also pose a substantial challenge. This is why employing superior data selection techniques and minimizing features are supported, they help eliminate unwanted chaos cutting through what matters the most.

What happens is that when data becomes excessively large, we often end up developing high-end predictive models that fails to deliver productive results. But, on the other hand, if you track the events, giving importance to validation and testing routines, the outcomes will spell perfection. And that’s what we are looking forward to.

Predictive Analytics is the Key

IoT has made predictive analytics a daunting reality. Owing to its critical business significance, predictive analytics is quickly accelerating along the priority ladder of IoT stakeholders. However, take a note, this breed of analytics may not be fruitful in every instance. It’s imperative to begin your analytics endeavor by clearly defining your module’s objective, followed by needed research and valuation.

Next, you need to sync in with subject matter pundits to ascertain which predictions will lead you closer to fulfilling the business objectives. Following to this, you have to be sure that you have all the data required to make prediction. In other cases, you can re-set goals, anytime.

Find the best Data Science Courses in Noida… At DexLab Analytics. Get detailed information on the website.

The blog has been sourced from — www.networkworld.com/article/3305329/internet-of-things/3-iot-challenges-that-keep-data-scientists-up-at-night.html

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Cyber Security with Data Analytics: Key to a Successful Future

Posted on September 18, 2018May 23, 2020 by Dexlab

Cyber Security with Data Analytics: Key to a Successful Future

Cyber security and data analytics are two dominant fields of technology that’s increasingly gaining a lot of importance. While data analytics helps in figuring out whether the latest campaign was successful or not, cyber security ensures all your confidential documents are stored in the cloud under supreme security and surveillance.

Nevertheless, learning them can be quite expensive and time-consuming. Especially so for the bosses, who like forever wonder if these in-demand courses would help their employees imbibe added skills and improved work expertise.

On the contrary, we would say attending data analyst courses in Delhi is not at all like a wager – in fact, in most cases, it turns out to be good bets for the bosses as their employees learn in-demand skills with which they strive for long-term wins for the company, pulling up the company’s fortune and future with them. So, not bad eh?

The Pathway to Success

Now, talking about the employment and work opportunities, if you ask which positions would fill up sooner, you’d most certainly hear: data analytics and cyber security. The world is in dire need of skilled data analysts; and trust us, when we say they are difficult to find, but harder to retain! Because mature talent is not an everyday affair, anymore. So, what happens next?

A majority of cybersecurity tool providers are adding ultra-functional data science capabilities to their cybersecurity platforms. This includes factoring behavior-based analytics and responses into antivirus suites, firewalls, and traffic analyzers – which, eventually turns the products and services smarter and effective. Another domain worth noticing is the artificial intelligence, which when fused with data science can augment conventional cybersecurity. Though the technology is still in its nascent stage, soon it’s going to garner attention and develop full-fledged.

Meanwhile, the frameworks of cybersecurity are evolving. This exposes the challenge of securing black-box algorithms – an incredible product of data science program that helps us learn and grow dynamically.

As these analytical models are so highly intricate as well as valuable for the companies, cybersecurity professionals need to be well-versed in all avenues of data science for ascertaining protection to these models, while ensuring integrity at the same time.

Conclusion

Therefore, the convergence of data science and cybersecurity is proved to be one of the trendiest areas of technology industry in the next few years. With regular innovations and technological evolution, be prepared to witness a surge in the demand for data science and cybersecurity professionals before it heads towards a near-term horizon.

So, start preparing yourself now and be ready to hone your skills in elusive cybersecurity practices and AI controls and models to stay ahead of the curve.

DexLab Analytics offers comprehensive data analytics certification courses for freshers as well as intermediates. Pick a particular course, train yourself and dig deeper into the world of analytics.

For more information, visit their official website today.

The blog has been sourced from —

vulcanpost.com/644684/data-analytics-courses-singapore/

tdwi.org/articles/2018/01/16/adv-all-cybersecurity-plus-data-science-future-career-path.aspx

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7 Leading Sectors in India That Need an AI & Analytics Makeover

Posted on August 30, 2018 by Dexlab

7 Leading Sectors in India That Need an AI & Analytics Makeover

Advancements in the field of data analytics and artificial intelligence are fuelling innovation in every nation around the world. India too is showing keen interest in AI. This year, the government has doubled the amount invested in the innovation program Digital India which drives advances in machine learning, AI and 3-D printing.

The signs of increased activity in AI research and development are showing in different areas. Here are the topmost sectors of India that are in dire need of AI and data science revolution:

FINANCE

According to reports by PricewaterhouseCoopers, financial bodies and payment regulators deal with billions of dollars in transactions through ATMs, credit cards, e-commerce transactions, etc. When human expertise is combined with advanced analytical methods and machine learning algorithms, fraudulent transactions can be flagged the moment they occur. This leaves less room for human errors. Considering the recent discoveries about major frauds in reputed banks in India, this approach seems more like a necessity.

Image source: American Banker

AGRICULTURE

Although 40% of the Indian population works in the agricultural sector, revenues from this sector make up only 16% of the total GDP. The agricultural industry needs advanced data analytics techniques for the prediction of annual, quarterly or monthly yields; analyzing weather reports are observing the best time to sow; estimating the market price of different products so that the most profitable crop can be cultivated, etc. AI powered sensors can measure the temperature and moisture level of soil. With the help of such data farmers can identify the best time to plant and harvest crops and make efficient use of fertilizers.

Image source: Inventiva

HEALTHCARE

According to the Indian constitution, each and every citizen is supposed to get free healthcare. And government hospitals do provide that to people below poverty line. Nonetheless, 81% of the doctors work for private hospitals and nearly 60% hospitals in India are private (According to Wikipedia). The root cause for this is that government hospitals are overpopulated. People who can afford healthcare services from a private hospital prefer to be treated there. Data science can play a pivotal role in managing the growing demand for healthcare services by strengthening the current infrastructure. It can help by predicting how many days a patient is likely to be admitted and find out the proper allotment of beds. AI fine tunes medical predictions and helps selecting a proper line of treatment.

Image source: wxpress

CRIME PREDICTION

Considering the number of security threats and extremist attacks India has faced in the past, there’s urgent need to develop efficient methods that can neutralize such threats and maintain proper law and order. AI and ML can step in to ease the burden of security personnel. A welcome development is the collaboration between Israeli company Cortica and Best Group. Massive amounts of data from CCTV cameras across the nation are being analyzed to anticipate crime and take action before it happens. Streaming data is scrutinized for behavioral anomalies, which are considered as warning signs for a person who commits a violent crime. The aim of the Indian authorities is improving safely in roads, stations, bus stops and other public places.

Image source: Digital Trends

From the paragraphs above it is evident that AI and data analytics has immense scope to improve these major sectors in India. While you look forward to these developments also follow DexLab Analytics, which is a leading data analyst training institute in Delhi. For data analyst certification, get in touch with DexLab’s industry experts.

Reference:

www.brookings.edu/blog/techtank/2018/05/17/artificial-intelligence-and-data-analytics-in-india

www.analyticsvidhya.com/blog/2018/08/top-7-sectors-where-data-science-can-transform-india-with-free-datasets

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Fundamental Concepts of Statistics for Data Science Beginners- Part One

Posted on August 17, 2018 by Dexlab

Fundamental Concepts of Statistics for Data Science Beginners- Part One

Do you aspire to be a data scientist? Then is it essential that you have a solid understanding of the core concepts of statistics. Everyone doesn’t have a Ph.D. in Statistics. And that isn’t the only way to excel in the field of data science. But yes, knowing stats well is a prerequisite for data science.

Nowadays, popularly used libraries, like Tesorflow, liberate the user from the intricacies of complex mathematics. Still, it is advisable to be familiar with the fundamental principles on which they work, because that will enable you to use the libraries better.

In this blog, we attempt to shed light on some basic concepts, theorems and equations of statistics for data science.

Statistical Distributions:

Statistical distributions are important tools that you must arm yourself with to be a skilled data scientist. Here, we shall talk about two important distributions, namely Poisson distribution and Binomial distribution.

Poisson distribution:
This distribution is used to find out the number of events that are expected to occur during an interval of time. For example, the number of page views in one second, the number of phone calls in a particular period of time, number of sales per hour, etc.

The symbols used in the equation are:

x: exact number of successes

e: constant equal to 2.71828 approximately

λ: average number of successes per time interval

Poisson distribution is used for calculating losses in manufacturing. Let us consider that a machine generates metal sheets that have ‘x’ flaws per yard. Suppose the error rate is 2 per yard of sheet (λ). Applying this information to Poisson distribution, we can calculate the probability of having exactly two errors in a yard.

Source: Brilliant.org

Poisson distribution is used for faster detection of anomalies.

Binomial distribution:

This is a very common distribution in Statistics. Suppose you have flipped a coin thrice. Using basic combinatorics for flipping a coin thrice, we see that there are eight combinations possible. We find out the probabilities of getting 0, 1, 2 or 3 heads and plot this on a graph. This gives us the binomial distribution for this particular problem. It must be remembered that Binomial distribution curve is similar to a Normal distribution Curve. Normal distribution is used when values are continuous and Binomial distribution is used for discrete values.

Source: mathnstuff.com

Binomial distribution is a discrete probability distribution where number of trials is predetermined and there are two possible outcomes– success and failure, win or lose, gain or loss. Depending on a few conditions, like the total number of trails is large, the probability of success is near 1 and the probability of failure is near 0, the trails are independent and identical, etc., the binomial distribution is approximated to a normal distribution.

Source: MathBitsNotebook

Binomial distribution has many applications in business. For example, it is estimated that 5% of tax returns for individuals with high net worth in USA is fraudulent. These frauds might be uncovered through audits. Binomial distribution is used to find out for ‘n’ number of tax returns that are audited, what is the probability for say 5 fraudulent returns to be uncovered.

There are some more probability distributions, like Bernoulli and Geometric distributions. We shall cover that and more in the following blogs. So, stay tuned and follow DexLab Analytics. The experts here offer top-quality data science courses in Delhi. Go through the data science certification details right now!

References:

upgrad.com/blog/basics-of-statistics-for-data-science

anomaly.io/anomaly-detection-poisson-distribution

analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science

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The Big Data Driven Future of Fashion: How Data Influences Fashion

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

The Big Data Driven Future of Fashion: How Data Influences Fashion

Big Data is revolutionizing every industry, including fashion. The nuanced notion of big data is altering the ways designers create and market their clothing. It’s not only aiding designers in understanding customer preferences but also helps them market their products well. Hadoop BI is one of the potent tools of technology that provides a wide pool of information for designers to design range of products that will sell.

How Does the Mechanism Work?

Large sets of data help draw patterns and obviously trends play a crucial role across the fashion industry. In terms of nature, fashion and trends both are social. Irrespective of the nature of data, structured or unstructured, framing trends and patterns in the fashion industry leads to emerging ideas, strategies, shapes and styles, all of which ushers you into bright and blooming future of fashion.

What Colors To Choose For Your Line?

KYC (Know Your Customer) is the key here too. A fashion house must know which colors are doing rounds amongst the customers. Big data tells a lot about which color is being popular among the customers, and based on that, you can change your offerings subject to trend, style picks and customer preferences.

Men’s or Women’s Clothing: Which to Choose?

Deciding between men’s or women fashion is a pivotal point for any designer. Keep in mind, target demographic for each designer is different, and they should know who will be their prospective customers and who doesn’t run a chance.

Big data tool derive insights regarding when customers will make purchases, how large will be the quantity and how many items are they going to buy. Choosing between men’s and women’s fashion could make all the difference in the world.

Arm yourself with business analyst training courses in Gurgaon; it’s high time to be data-friendly.

Transforming Runway Fashion into Retail Merchandise

Launching a brand in the eyes of the public garners a lot of attention, and the designs need to be stellar. But, in reality the fashion that we often see on runways is rarely donned by the ordinary customers; because, the dresses and outfits that are showcased on the ramp are a bit OTT, thus altered before being placed in the stores. So, big data aids in deciphering which attires are going to be successful, and which will fail down the line. So, use the power of big data prudently and reap benefit, unimaginable across the global retail stores.

Deciding Pricing of the Product

As soon as the garbs leave the runway, they are tagged with prices, which are then posted inside the stores, after analyzing how much the customers are willing to pay for a particular product. For averaging, big data is a saving grace. Big data easily averages the prices, and decides a single mean price, which seems to be quite justifiable.

However, remember, while pricing, each garments are designed keeping in mind a specified customer range. Attires that are incredibly expensive are sold off to only a selected affluent user base, while the pricing of items that are designed for general public are pegged down. Based on previous years’ data, big data consultants can decide the pricing policy so that there’s something for all.

The world of fashion is changing, and so is the way of functioning. From the perspective of fashion house owner, collect as much data as possible of customers and expand your offerings. Big data analytics is here to help you operate your business and modify product lines that appeals to the customers in future.

And from the perspective of a student, to harness maximum benefits from data, enroll in a data analyst course in Gurgaon. Ask the consultants of DexLab Analytics for more deets.

The article has been sourced from –

channels.theinnovationenterprise.com/articles/8230-big-data-hits-the-runway-how-big-data-is-changing-the-fashion-industry

iamwire.com/2017/01/big-data-fashion-industry/147935

bbntimes.com/en/technology/big-data-is-stepping-into-the-fashion-world

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5 Trends Shaping the Future of Data Analytics

Posted on July 24, 2018May 23, 2020 by Dexlab

5 Trends Shaping the Future of Data Analytics

Data Analytics is popular. The future of data science and analytics is bright and happening. Terms like ‘artificial intelligence’ and ‘machine learning’ are taking the world by storm.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020, says Forbes, a leading business magazine.

Last year, at the DataHack Summit Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton shared some slivers of knowledge in the illuminating field of data science. He believes that the following trends will shape up the world of data analytics, and we can’t agree more.

Dive down to pore over a definitive list – thank us later!

Internet of Things (IoT)

Does IoT ring any bell? Yes, it does, because it’s nothing but evolved wireless networks. The market of this fascinating new breed of tech is expected to grow from $170.57 billion in 2017 to $561.04 billion by 2022 – reasons being advanced analytics and superior data processing techniques.

Artificial Intelligence

An improved version of AI is Augmented Intelligence – instead of replacing human intelligence, this new sophisticated AI program largely focuses on AI’s assistive characteristic, enhancing human intelligence. The word ‘Augmented’ stands for ‘to improve’ and together it reinforces the idea of amalgamating machine intelligence with human conscience to tackle challenges and form relationships.

Augmented Reality

Look forward to better performances and successful models? Data is the weapon of all battles. Augmented Reality is indeed a reality now. The recent launch of Apple ARkit is a pivotal development in bulk manufacturing of AR apps. The power of AR is now in the fingertips of all iPhone users, and the development of Google’s Tango is an added thrust.

Hyper Personalization

#KnowYourCustomer, it has become an indispensable part of today’s retail marketing; the better you know your customers, the higher are the chances of selling a product. Yes, you heard that right. And Google Home and Amazon Echo is boosting the ongoing operations.

Graph Analytics

Mapping relationships across wide volumes of well connected critical data is the essence of graph analytics. It’s an intricate set of analytics tools used for unlocking insightful questions and delivering more accurate results. A few use cases of graph analytics is as follows:

Optimizing airline and logistic routes
Extensive life science researches
Influencer analysis for social network communities
Crime detection, including money laundering

Advice: Be at the edge of data accumulation – because data is power, and data analytics is the power-device.

Calling all data enthusiasts… DexLab Analytics offers state of the art data analytics training in Gurgaon within affordable budget. Apply now and grab amazing discounts and offers on data analyst course.

The article has been sourced from – yourstory.com/2017/12/data-analytics-future-trends

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How American Express Uses Data Analytics to Promote a Data-Driven Culture

Posted on April 26, 2018May 23, 2020 by Dexlab

Since 2010, American Express, with an encompassing database crossing over 100 million credit cards accounting for more than $ 1 trillion in charge volume annually, is harnessing the power of big data. Undeniably, it resulted in incredible improvements in speed and performance.

In the last four decades, the entire financial services industry has undergone a massive change, notably in the spheres of:

Electronic payments – Online payments, comprising credit and debit cards have dramatically increased over cash, globally.

E-commerce – An excessive reliance on smartphones and internet have boosted E-commerce capabilities manifold times.

With an increasing interaction between company and customers, the latter’s online and offline identity is being collaborated for an encompassing 360-degree view. This eventually drives innovation in product designing and marketing.

Formulating a Data-Driven Culture

Data analytics is like the bull’s eye of effective marketing, and servicing and risk management. Data curation and management is now a prerequisite for competitive excellence.

Since its inception, American Express flaunts transformation: the company has transformed itself from being a trivial freight forwarding business to a top notch player in payments and customized service industry. Over the years, the working mechanism of the firm has changed dramatically, and today, it is #1 small business card issuer in the whole of the US.

No matter, while the company strives to evolve, its core values remain somewhat same. Keeping their customers above anything else and behave like a good citizen are two core values of American Express that are beyond alterations. To become a successful data-driven organization, they believe in investing on technology, analytics, along with human talent, emphasizing on a proper synthesis between technology and human cognition to trigger robust growth and future success.

How American Express Stays Relevant and Fresh?

Risk 2020 – American Express envisions how an economy or marketplace might look like after a few years, and in the process, assesses the risks to combat to address the weaker issues in the economy. A comprehensive approach, including cloud, deep learning, mobile computing and AI is the solution.

Cornerstone – This is an encompassing, global big data ecosystem. The data is stored and shared with global potentialities across trusted sources. In any organization, data is the centre of attraction, and the consultants at American Express recognize the essence of innovation lies at company’s DNA and not somewhere on the top.

The data-driven culture in American Express is simple, natural and nuanced. A huge data base is created, from acquisition to customer management, which eventually needs to be shared with third parties and partners to derive insightful conclusions for better customer experience and risk assessment. “At American Express, we take our responsibility to serve customers and the public seriously, always ensuring that solutions are best-in-class and valuable to our customers,” says Ash Gupta, president, Global Credit Risk & Information Management, American Express.

“American Express’ closed-loop data allows us to analyze a large volume of real spending that can help marketers across a range of industries connect with customers and provide unique value,” he further adds.

To know more about data-driven customer experience, visit DexLab Analytics, a premier data analyst training institute in Delhi. They offer a plethora of data analyst training courses for interested candidates.

The blog has been sourced from:

https://www.forbes.com/sites/ciocentral/2018/03/15/how-american-express-excels-as-a-data-driven-culture/#5c5ed1a81635

https://digit.hbs.org/submission/american-express-using-data-analytics-to-redefine-traditional-banking/

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Watch Out: Top Retail Trends 2018 That Might Redefine Industry Goals

Posted on March 24, 2018May 23, 2020 by Dexlab

They must change – retailers finally understood this basic but true fact. For years, the retail honchos were averse to change – they preferred everything to be smooth and consistent like they were in previous years.

Now, the retail game-play is changing altogether. Today, it’s the customer who defines the entire shopping experience. No longer, storing data in traditional silos is termed as a viable option – the integration of omni-channel trade and tech-inspired merchandizing is the go-to option. Already, several well-funded retailers and global store giants are on their way to exploit the data power – they are adjusting their working mechanisms and resorting to assortment and innovations because that’s the only way to survive and sail away!

Looking ahead, here are some of the biggest retail trends to watch in 2018:

A dramatic evolution in technology

Technological transformation holds a fresh can of possibilities for retailers, but its implementation demands a lot of attention. While 2017 was reckoned to be the year of digital discovery, 2018 is going to be the year when retailers will adapt with the changing market and experience evolution in their customer’s needs. Hence, evolution will be the key to success.

Opportunities in AI are also on the rise. Chatbots, robotics and facial recognition and image recognition technologies are unleashing robust opportunities this year. Retailers are hoarding large chunks of data to curate personalized experiences for customers, and win their hearts away. More data means improved algorithm performance, and the best thing is that retailers are going on generating significant amounts of data, through both offline and online mediums. Artificial intelligence in retail can be utilized in many ways, right from improving product specifications and enhancing customer service experience.

Artificial intelligence coupled with machine learning and Internet of Things supports customer experience – there exists amazing opportunity for retailers to gain by using these new age concepts. For better data utilization, get yourself an excellent data analyst training from DexLab Analytics.

Mobile payments will usher us into a cashless economy

China has already gone cashless; thanks to AliPay and WeChat Pay. Following that, the rest of the world is looking up to the likes of Amazon Pay, Walmart Pay, Apple Pay and other types of cryptocurrencies. It’s only a matter of time before global consumers replace their plastic debit cards with more efficient and faster mobile payment options.

Work on improving offline experiences too

Not only online, but retailers should consider looking into offline experiences – how they can keep shopping as human, real and visual as possible. The mode of shopping might be transforming, but humans and their preferences are still the same. Customer experience is still important and offline experience will just focus on that.

Robotic retail is scaling up

In the E-commerce industry, the robot to human ratio is fast changing. While Walmart is testing retail robots, drone delivery is increasingly becoming popular and a viable solution. By 2020, its predicted consumer facing robots will show up in retail stores, all over.

Improvements in technology mean a lot of retail growth. And when its technology, we can’t leave behind DATA. It’s like the new currency in the retail scenario – for a comprehensive Retail Analytics Courses, visit DexLab Analytics.

The article has been sourced from:

https://www.forbes.com/sites/pamdanziger/2017/12/27/retail-shopping-predictions-2018/#1116fcdafb33

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How to Develop a Data-Driven Culture among the Employees and Organization?

Posted on March 21, 2018May 23, 2020 by Dexlab

Data creation and consumption is exploding. So is the challenge of analyzing data and transforming it into actionable insights.

The expert consultants at IBM say – 90% of the data in the world has been created in the last couple of years at a rate of 2.5 quintillion bytes per day. Just imagine, when such gold mines of information go unused, what toll does it take on companies who harbors them? The loss is insurmountable, isn’t it?

But the question that makes us brood is how do companies empower employees to use such vast pools of data to reveal hidden business insights?

To keep pace with an accelerating growth of data generation and robust competitive landscape, new age companies need to shift their focus to sound Business Intelligence solutions that helps in collecting and analyzing data to determine patterns and alert users, in case of anomalies in the nature of business. And the good news is that they are doing so.

Once companies start off with data handling and data mining, the issue of utilization needs to be addressed next – the trickiest problem with data utilization is the insights that take place within the departments –while creating data silos. Siloed data results in creating a lot of issues, but the larger one is that it creates only a partial view of whatever is happening within an organization and a bigger picture is not available.

As a result, a data-driven culture is to be adapted – but how?

Right from the top-level

The true DNA of any organization lies within its top-level management team, including the founders and managing directors – and that’s where the foundation stone of data-driven culture should be implanted. Implementing something new and offbeat is intimidating, but when the company leaders promote it, the idea gains familiarity and merges with other data-driven decisions.

Empowerment

Employees need to feel empowered – then only they can independently mine data and share crucial findings with colleagues and seniors without asking for help from the IT guys. A common misconception that exists around is that data analytics is a cumbersome task which requires heavy involvement from IT- but in reality, things are changing – BI tools are being revamped to make users more independent and self-sufficient to take better business decisions.

Time and again, it’s important for the executive management to show some token of appreciation to employees for their hard work. It is in these subtle ways the data-driven culture gets promoted across company walls.

Sharing is caring

Now, the data siloes come into the picture. Once employees are comfortable with using data and bearing the fruits of success – insights need to be shared across the business fronts to draw a much larger picture. Not only it promotes cross-team and departmental collaboration, but also brings in newer data into the limelight that wouldn’t have been possible before. Hence, sharing of insights is crucial for business success.

From the above discussion it is clear, insights gained from data is extensively beneficial for business, as they offer new answers for innovation and development. However, achieving data nirvana is no mean feat – the steps highlighted above should be followed, and then only the companies would be able to achieve their desired goal, i.e. a seamless data-driven culture.

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Sources: http://dataconomy.com/2017/05/data-nirvana-develop-data-driven-culture

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