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Data Driven Projects: 3 Questions That You Need to Know

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 fromdataconomy.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

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.

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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 fromwww.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?

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.

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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.

For R Predictive Modelling Certification, join DexLab Analytics. It’s a premier data science training platform that offers top of the line intensive courses for all data enthusiasts. For more details, visit their homepage.

 

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

 

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Best Data Science Interview Questions to Get Hired Right Away

Best Data Science Interview Questions to Get Hired Right Away

Data scientists are big data ninjas. They tackle colossal amounts of messy data, and utilize their imposing skills in statistics, mathematics and programming to collect, manage and analyze data. Next, they combine all their analytic abilities – including, industry expertise, encompassing knowledge and skepticism to unravel integral business solutions of meaningful challenges.

But how do you think they become such competent data wranglers? Years of experience or substantial pool of knowledge, or both? In this blog, we have penned down the most important interview data questions on data science – it will only aid you crack tough job interviews but also will test your knowledge about this promising field of study.

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What do you mean by data science?

Data is a fine blend of statistics, technical expertise and business acumen. Together they are used to analyze datasets and predict the future trend.

Which is more appropriate for text analytics – R or Python?

Python includes a very versatile library, known as Pandas, which helps analysts use advanced level of data analysis tools and data structures. R doesn’t have such a feature. Therefore, Python is the one that’s highly suitable for text analytics.

Explain a Recommender System.

Today, a recommender system is extensively deployed across multiple fields – be it music recommendations, movie preferences, search queries, social tags, research and analysis – the recommender system works on a person’s past to build a model to predict future buying or movie-viewing or reading pattern in the individual.

What are the advantages of R?

  • A wide assortment of tools available for data analysis
  • Perform robust calculations on matrix and array
  • A well-developed yet simple programming language is R
  • It supports an encompassing set of machine learning applications
  • It poses as a middleman between numerous tools, software and datasets
  • Helps in developing ace reproducible analysis
  • Offers a powerful package ecosystem for versatile needs
  • Ideal for solving complex data-oriented challenges

What are the two big components of Big Data Hadoop framework?

HDFS – It is the abbreviated form of Hadoop Distributed File System. It’s the distributed database that functions over Hadoop. It stores and retrieves vast amounts of data in no time.

YARN – Stands for Yet Another Resource Negotiator. It aims to allocate resources dynamically and manage workloads.

How do you define logistic regression?

Logistic regression is nothing but a statistical technique that analyzes a dataset and forecasts significant binary outcomes. The outcome has to be in either zero or one or a yes or no.

How machine learning is used in real-life?

Following are the real-life scenarios where machine learning is used extensively:

  • Robotics
  • Finance
  • Healthcare
  • Social media
  • Ecommerce
  • Search engine
  • Information sharing
  • Medicine

What do you mean by Power Analysis?

Power analysis is best defined as the process of determining sample size required for determining an impact of a given size from a cause coupled with a certain level of assurance. It helps you understand the sample size estimate and in the process aids you in making good statistical judgments.

To get an in-depth understanding on data science, enroll for our intensive Data Science Certification – the course curriculum is industry-standard, backed by guaranteed placement assistance.

The blog has been sourced fromintellipaat.com/interview-question/data-science-interview-questions

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3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

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:

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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

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?

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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

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

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.

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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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Business Intelligence: How to Enhance User Adoption?

Business Intelligence: How to Enhance User Adoption?

For business modernization, smart business intelligence solution is the key. Getting to the crux and leveraging vast pools of data that companies gain access to triggers encompassing digital transformation. BI tools not only let companies grasp the data but also develop actionable insights to smoothen the impactful decision-making capabilities and take companies towards future progress.

It’s not an out of ordinary kind of concept, for half a decade, companies have been utilizing these kinds of tools for better efficiency and productive outcomes, yet user adoption for BI tool remains relatively low.

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Reasons behind Lower User Adoption of Business Intelligence:

Guys at the helm of company affairs, including Chief Information Officers, Chief Technology Officers and Chief Data Officers may think it’s high time to incorporate Business Intelligence tools for smarter operations, but it may not have the same effect on the employees. Employees may not be much inspired!

It holds truer, especially for those employees, who have been in the workforce for long and haven’t for once used such intricate, new-age tools to decipher what data says. For them, old is gold – they prefer to continue their own kind of data analysis in the same way they have been doing for so many years.

How Companies Can Improve Data-Driven Mindsets?

In order to be ahead of the curve, the data mindset of the workforce needs to be changed. If businesses have to be completely data-driven, they can’t just take Business Intelligence lightly.

Here are a few ways business can drive user adoption of BI:

Introduce BI as a necessity, not luxury

Once understanding company data was considered as an added advantage to normal work procedures. But, in this age of digital transformation, it’s no longer a luxury but a necessity. And sooner the employees realize this, the better it becomes.

Employees across organizations should have thorough access to data. It boosts decision-making. By going completely data-driven, business intelligence user adoption will automatically improve. Along with employees, businesses too will benefit a lot from such adoption.

Promote Favorable Impacts of BI

Putting light on success stories of BI implementation helps! It’s being regarded as a powerful way to encourage budding data scientists and already in-workforce employees: the powerful impression of BI and its significant impacts on key performance indicators will tell a different story to the world.

The best way of doing it would be by developing an internal case study that will elucidate how a team after incorporating Business Intelligence fulfilled their desired organizational goals. For best results, let a manager or C-level employee present the case study to the workforce. Surely, this will enhance levels of user adoption of BI.

Continuous Training is a Must

Business Intelligence calls for no one-track solutions; the concept deals with almost endless opportunities, which means continuous training initiatives should be taken up to explore every facet of this cutting edge tool.

When an employee have deeper knowledge about a particular tool, they are more likely to derive maximum benefits out of it. So, by giving continuous training, through various FAQs, webinars and video tutorials, employees can now become very easily completely data-driven.

Now, following these easy yet effective tips, business leaders can increase their lower rates of BI adoption and stride towards full digital transformation of their companies, triggering impactful future goals.

Want to know more about Data Science Courses in Noida? Drop by DexLab Analytics; for a fulfilling learning experience, opt for their Data Science Courses. They are simply excellent and student-friendly. 

 
The blog has been sourced from — www.sisense.com/blog/make-business-intelligence-necessity-drive-user-adoption
 

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