The insurance industry strives a lot to win the trust of their customers. Imagine a scenario where the right ones are not rewarded and the wrong ones are rewarded. This leads to chaos or loss of faith in a system.

Now the question is ‘what breaches the trust utmost’? In any business, it is the money that matters in return for the investment that the customer paid for a service or product. When it comes to insurance, customers expect a reward for the premium they paid. During risks/accidents/deaths they expect the reward from the insurer during the claims process. Because of this reason, claims processing is the most crucial stage for the insurers. It should be seen in the context that, not all those who claim are not genuine ones, but at the same time the genuine claims need to be rewarded. It is at this stage fraud analytics powered insurance processes comes to the aid of insurers.

Why is it Important to Detect Fraud?

According to FBI, about $45 billion is lost every year in insurance fraud. Handling claims is not a simple process. On the other, it is the most complex process in the insurance processes. Complexities include frequent file transfers, gathering adequate information, sieving misinformation from the right ones, etc. All these contribute to the complexities for the insurers. If the insurance carrier is unable to identify whether the claims are genuine or fraud, it is a foregone conclusion that the insurance carrier will undergo huge loss leading to a chain of losses to other sectors linked with it.

Today, customers are looking at quick and quality services. Customers may not like a scenario where more time is required to conduct background checks. Even if the insurers use the best communication to soothe their feeling, customers leaving a sad note or unimpressed, can send wrong feedback on the social media. To avoid all such instances, the answer lies in fraud analytics. Technically speaking with the help of analytics insurance carriers need to integrate data sourced from claims notes, telematics data, social media, OFAC (Office of Foreign Assets Control), weather data etc., inspectors can develop pattern recognition algorithms to speed up the claims process. While developing the algorithms, reliable high-quality data is identified and correctly integrated with all the meta-data labels. The process includes analyzing, filtering and segmenting by a computer-based system that analyses various risks.

Ultimately the bottom line is to provide nearly automatic clearance for simple, straightforward cases, and immediate expert attention on the complicated or suspected claims. Analytics also helps in streamlining the internal processes. This will result in saving the valuable time of the customers. If there is a possibility to reduce the waiting period of the customers with fraud analytics, they will hail this as a big success in the service area.

Thanks to analytics, today insurance executives are empowered to make informed decisions for strategizing, carving new niche markets, as well as building loyal customers. Fraud analytics is promising for insurance carriers as it has the capability to respond to the evolving insurance industry.

According to Coalition Against Insurance Fraud anti-fraud alliance, speaking for customers, fraud accounts for 5-10 percent of claims costs for insurers in U.S. and Canada. Nearly one-third of insurers (32 percent) agree fraud constitute to 20 percent of claims costs.

To Pursue Path to Profit Power Insurance Processes with Fraud Analytics

For insurance carriers, the main issue is to raise profits amidst tough competition. The facts and figures above mentioned states fraud constitute 20% of claim costs. Take the example of P&C industry to understand the impact of the loss. The insurance Information Institute says that the profit of P&C insurance industry income analysis, from 2010-2014 is $55.5 billion. If 20% can be added as profits through fraud analytics, it will immensely help the industry to contribute to the GDP of the nation benefitting all stakeholders. Therefore insurance carriers who are pursuing to install trust in their customers must power insurance processes with fraud analytics.

Data analytics has become indispensable to many industries. It is firmly embedded in the way a lot of companies to do business. Most companies use the power of data analytics to study customer behavior and manage progress of most business process. This provides opportunities for firms to offer services in this domain. One such service is the financial audit. Audit firms can really benefit from this opportunity as the technology evolves and advances in the coming times. However, listed below are the key reasons why the audit services providers should gravitate towards data analytics.

1. Volume of data – Traditionally firms offering services to review the financial data, traditionally used to just test data entries based on certain as it is not possible to go through all the data manually. This left a huge gap in the analysis that was done, even if performed by CPA certified professionals. The data analytics approach allows such teams to be able to test all the data extracted from their clients ERP systems and provide much more conclusive analysis in a much shorter turn-around time.

2. Sensitive Nature of the data – Recent times have shown how important it is to have proper checks and balances on the financial industries. Even small discrepancies over time can affect lives of numerous people just like it did during the financial crisis of 2008. Even at the level of public companies, it is important to ensure that the firms to do not engage in illegal financial activities in order to mislead the shareholders and stakeholders.

3. Additional Revenue Stream – Most firms are able to generate new revenue streams by promising much more comprehensive audit reviews for their clients. The inclusion of data analytics to support the audit approach allows for firms to offer a wide array of services to their clients. It serves both the firms and the clients well. It allows the firms to increase the scope of the audit and the clients can be informed by timely to take corrective actions.

Audit firms sit at a very advantageous position to benefit from all the advancements in the field of data analytics. Not only can they vastly improve their existing processes to provide much more conclusive analysis but also drive innovation to offer a variety of new services to their clients. It is a win-win for everyone involved in this game. It is just a matter of lining up the ducks.

What is Data Analytics? The simplest definition of analytics is “the science of analysis.” However, a practical definition would be how an entity, e.g., a business, arrives at an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making, but unless data is considered, it would not be an analytical decision-making process.

Analytics have been used in business since the time management exercises that were initiated by Frederick Winslow Taylor in the late 19th century. Henry Ford measured pacing of the assembly line, thus revolutionizing manufacturing. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications

Today, businesses of all sizes use analytics. For example, if you ask my fruit vendor why he stopped servicing our road he will tell you that we try to bargain a lot and hence he loses money, but on the street next to mine he has some great customers for whom he provides excellent service. This is the heart of analytics. Our fruit vendor TESTED servicing my street and realized that he is losing money – within one month he stopped servicing us, and even if we ask him, he will not show up. How many businesses today know who their MOST PROFITABLE CUSTOMERS are? Do they know who their MOST COST GENERATING customers are? And given the knowledge of most profitable customers, how should you target your efforts to ACQUIRE the MOST PROFITABLE customers?

Large business uses analytics to drive the entire organizational strategy. Some examples include:

• Capital One, a credit card company in the U.S., uses analytics to differentiate customers based on credit risk and they match customer characteristics with appropriate product offerings.

• Harrah’s Casino, another U.S. company, identified that against popular belief, their most profitable customers are the ones playing slots. To leverage this insight, they have created marketing programs to attract and retain their MOST PROFITABLE CUSTOMERS.

• Netflicks, online movie service, identifies the most logical movies to recommend based on past behaviour. This model has increased their sales because the movie choices are based on customers’ preferences and therefore the experience is customized to each individual.

Common applications of analytics include the study of business data using statistical analysis to discover and understand historical patterns with an eye to predicting and improving future business performance. Also, some people use the term to denote the use of mathematics in business. Others hold that the field of analytics includes the use of operations research, statistics and probability; however it would be erroneous to limit the field of analytics to only statistics and mathematics.

While the concept is simple and the notion is intuitive, the common leverage of analytics to drive business is just in its infancy. Stay connected to know more about the Science of Analytics in the second part of this article.

One of the latest and advanced technology trends in vogue is the Big Data Analytics training. This stream of technological improvement concerns an advanced process of collecting, managing and analyzing a bulk amount of facts.

The term ‘Big Data’ refers to the kind of facts that is so vast and complex that it is difficult and cumbersome for conventional facts tools to capture, analyze or store them. In the course of its application, huge info lets analysts mark the trends, obtain insights and go for relevant predictions. Leading companies owning international acclamation such as Amazon, Walmart, and eBay have been known to deal with huge info in their operations of late.

Hence, there has been a noticeable expansion in the job scope for skilled professionals for capturing and analyzing the huge info sets.

Relevance of handling on huge info

The term ‘Analytics’ is a much more familiar one to us as compared to the term ‘Big Data’, which is a more recent coinage. The prime reason behind the emergence of Big Data Analytics training is undoubtedly the increasingly vast and cumulative volume of facts that is being generated with multiple effects each day. The giant increase in this volume of facts is pacing competently with every walk of advancement if modern technology and civilization.

Huge info exercise concerns analysis of large volumes of facts that are in the order of terabytes and petabytes. In the US stock markets, shares worth billions are traded each day. While Walmart collects petabytes of files from customer transactions every hour, over thousands of credit card transactions are made all over the world every second.

Such trends have engendered a growing requirement to provide professionals with apt handling on huge info analysis.

Numerous Professional Organizations have taken up helps to promote and provide training on Big Data analytics

Different corporations and exercise companies have reportedly set foot on propagating a wide range of certification and analysis handlings on huge info. These preparation courses are run with the objective of providing an in-depth and comprehensive overview of Huge info, how its management and analysis can be executed with professional dexterity and efficiency by using effective tools such as SAS and R language. There are also several other such user-friendly tools which enable ease of access and operation.

Prominent role of NIIT and GNIIT in providing professional preparation on Vast Data

Keeping a keen eye on the growing demand of the current hour, it has set to offer efficient handling courses to professional enthusiasts. It has taken sincere notice of the requirement of IT professionals and hence, is providing huge info handling with special attention in the area of advanced analysis.

The courses offered by NIIT’s Statistics Analytics experts have been building up learners with appreciable proficiency in the concerned field, enabling them to escalate on their career path with specialized adroitness in handling huge info, which has become a prior requisite in most of the recognized organizations of today.

For those willing to build a career in the field of Business analysis, it has designed proficient handling programs in IT and Business analysis. The programs have been designed to effectively impart statistics analyzing capability to the learners so that they are confident to work exercising personal productivity tools, creative thinking and other relevant skills.

Students in the first year of their college experiences often have very little idea as to what career they want to choose. This can be frustrating if there isn’t enough clarity available to students to make informed choices. One particular area where students often seek clarity is when trying to understand the difference between software development and data analyst as career choices. The confusion often arises from the fact that there is an overlap with regards to programming skills. Listed below are some of the key differences between Software development and Data Analytics:

1. Scope – Software development as a service creates software for a firm whereas data analysis is more academic in nature with the goal to answer questions based on data collected from the field. As a matter of fact both these skills can be applied to different facets of a similar problem. Consider the case where there may be a need to automate certain tasks in a certain process in an organisation. In such a scenario, software developers would be brought in to create the relevant tool to accomplish the goals however, data about the existing usage can help point out the urgency around each of the tasks that need to be automated and point out the efficiencies that may be achieved for each task. With the combination of these skills and information, firms can prioritise their transition to automation.

2. Methodology – Software development models have been designed and pretty static in the execution. For data analysts, methodologies are often experimental and change frequently depending on the scope of the hypothesis. These methodologies are often used by economists and mathematicians. These have been brought into the field of data analytics.

3. Skills – This is the area which creates most doubts among young students. The technical skills required for these professions have quite a significant overlap. Although both require specialised training for certain aspects. For example, a software developer would need a specialise expertise on design elements like UI design and database schema layout. For data analytics professional would be more focussed on creating test cases for the ‘hypothesis’ being examined, which would require understanding of database querying at most.

To conclude, it would be fair to say that software development is the profession to create software from scratch, whereas in the profile of a data analyst, a software tool like SAS or Audit Command Language Software in often used to create analytical reports. Data analytics wouldn’t exists as a profession without software development which has enabled the creation of the software tools which allow replication or creation of mathematical models to perform analytics.

In order to move further towards value based care it is necessary for organizations to make more use of data analytics. In order to improve outcomes for patients at a lower cost with higher patient satisfaction, it is necessary to collect data and analyze it. There are many ways to do this and I will point out a few, including some that I use.

Healthcare is awash in data. There is data from billing, including diagnostic and procedure codes. There is claims based data from payers. There is much more detailed data in EHR’s, such as test results. This data is clinical data. Financial data is also important. Much of this data can be found in financial software such as QuickBooks and patient management software.

To make effective use of this data it is necessary to organize it, graph it, and investigate for significant changes. Each of these requires the use of statistics, either at an elementary level for spotting trends or at an advanced level to see if there are real and significant changes that indicate that the provider is moving towards providing better care at a lower cost. The examination and analysis of data gathered from patients and grouped by patient characteristics is termed population level health management.

One approach to using analytics to improve patient health is examine and track key indicators for patients with chronic diseases, usually the patients that incur the greatest cost in a practice. For instance, a practice can collect and analyze the A1c levels of all patients diagnosed with diabetes. This data can be collected each month and a mean and standard deviation determined. A month by month graph using an Excel spreadsheet will help visually indicate any trends that are occurring. If over a period of several months there is an upward trend, action can be taken to disrupt the trend. This could be making use of a nurse coordinator to help patients better manage their diabetes. One could use Control Charts from engineering statistics to better analyze whether trends are real or due to random fluctuations which are normal in any data collection over a time period. One could also use statistical t-tests to determine whether the changes are truly significant or not.

It can be very useful to graph data collected such as A1c levels, plotting the means over time as indicated above, and present them in a Dashboard with brief descriptions and analysis. These dashboards can be shared in a practice in order to drive improvement. This can be very effective in making improvements with a care team. I recently listened to a NPR podcast from ‘The Hidden Brain’ that described how a hospital in Pittsburgh improved its handwashing by care givers before entering patient rooms. The rate hovered around 10% for a long time in spite of repeated education of care givers. Then, the hospital started displaying monthly statistics of handwashing in a dashboard that all could access and see. Management focused care givers attention on the dashboards. Handwashing rates quickly improved to 90% and stayed there. The visuals made a significant impact on awareness of handwashing by providers.

Analytics can also be used to make improvements in patient satisfaction scores. The Medical Group Management Association (MGMA) provides a very good patient satisfaction survey for its members. I have adapted it to different providers depending upon their demands. The survey covers 36 basic questions and ends with “Would you recommend the provider to other?”, a very good ending question. I also add demographic questions. Providers can use the survey with patients and track performance in five areas-making the appointment, quality of front office staff and billing, ease of communication, visit with clinicians, and state of facilities. The goal is to have as a high as possible composite score for each of these areas. With advanced analysis more can be found out to improve satisfaction. It is possible to identify which of the questions have the most impact on the last question. It has been found that patients who are most likely to refer a practice to friends and family are usually the most satisfied. Thus, finding out which of the many questions have the most impact on this can help identify which areas need improvement. Analysis of which questions have the greatest impact should be carried out on a regular basis as the ones that have the greatest impact can change over time.

Of course, a dashboard should be created to report to staff the results of the surveys each month or so. This will help incentivize the staff to perform even better. If management likes it may break down the results by staffing area or by provider to help identify where individuals may improve. Individual coaching can then be used to help staff make improvements. Never should the dashboards be used to berate staff though. Whether individual dashboards are shared with other staff depends upon how well the staff functions as a team, how mutually supportive they are.

The MGMA collects a lot of data from its members through surveys. It then provides data for benchmarking to its members, some for free and some for costs. Providers who participate in their surveys can often get the results for free. In a recent article in its monthly publication Connection it provided a dashboard of data on the most profitable independent practices. It found, contrary to common sense, that those organizations with the highest median costs per FTE physician also were the most profitable. Some of the data in the dashboard follows:

· Median total medical revenue per FTE physician = $1,169,542

· Median total operating cost per FTE physician = $630,680

· Median total physician compensation and benefit = $462,722

· Median total support staff per FTE physician = 5.12

The article, Designing the practice of the future, found in the March 2017 issue of the magazine, also provided the benchmark data for all multispecialty practices. This data along with other data in the article and the article analysis can help providers develop long term strategies to improve the profitability of their practice.

As you can see there are many ways to use data and data analysis to improve the outcomes at your care site. It is important to identify which data to collect and analyze to make the most significant impact. As I said care providers are awash in data and trying to analyze it all would be a waste of time and energy. Those just beginning to use advanced analytics should start off with a few data projects and expand as time goes on. Intelligently using data and analysis can make a significant impact on the care that you provide and on your bottom line.

Whether you’re looking for a course to further your career or you have a keen interest in analytics and want to enjoy a long term career doing what you love, you will find that a business analytics certification can provide you with a host of benefits that you can enjoy on a personal level each and every day.

Not everyone loves being glued to a computer screen managing analytics, but there are those that find this fascinating and once you have the relevant training, you can use this daily to prove your worth and advance in your career.

The main benefit to holding a business analytics certification is that you can prove your competence to do the job. When applying for a position that requires this type of training, including your certification is a sign that you are more than able to do the duties required and increasing your chances of being selected.

You will find that your business analytics certification is recognized by industry professionals, which is a major benefit especially when applying for a position or a promotion within your company. You will find it is recognized, not only by company owners and directors, but also by your peers and colleagues.

Another benefit to holding this type of certification is the advancement in career potential that it can provide. When you start in any type of job, you begin at the bottom and you work your way to the top. You can only be considered for advancement if you have the relevant industry knowledge, experience and training. Holding a certification of this nature proves that you are more than able to manage the duties of the promotion and this can help you achieve success in your career moving forward.

When completing your course and being awarded with your business analytics certification you can enjoy a sense of accomplishment, a personal satisfaction that you have achieved something, improved your knowledge and have learned something new. Personal satisfaction is important to improve confidence and self-esteem and to help you be the best you can be in your chosen career on a daily basis.

You will notice that when you choose a business analytics certification course your performance will improve as you move through your career, this is due to the fact that any uncertainty on how to do your duties is removed. Your confidence is soaring and you know exactly what needs to be done in order to achieve success in your chosen tasks, which is a major benefit on its own.

In addition to this, having a business analytics certification can help you improve your income potential. With a certification in hand, your chances of getting paid a higher rate is increased. In this day and age, you want to earn the best possible income your position and level allows and companies are more likely to offer a higher income to someone who has a recognized certification than someone who has taught themselves and don’t hold that valuable piece of paper that proves they have received adequate training to complete the job at hand accurately and to the highest standard.

The good news about business analytics certifications is that you can complete them online at your own pace. This enables you to work full time and improve your knowledge and experience in your own time after hours without effecting your current position.

Big data refers to huge volumes of structured and unstructured data; however, processing such massive volumes of data via traditional data management tools is inefficient and impossible. To understand big data you have to realize the devices that are collecting it today e.g. bar code scanners, mobile cameras, CCTV cameras, motion sensors, smoke alarms, web analytical tools, CRMs, etc. From the examples, you can see that these devices collect a vast array of data types hence the structured and unstructured part in the definition. The sheer velocity at which the data is being produced cannot be controlled and processed using traditional methods and tools.

However, the use of big data and incorporation of big data analytical technology gives businesses the competitive edge over their competitors.

Big Data and Small Businesses

It is only a thing of the past when terms like big data and business intelligence were associated with large enterprises only. Today, small businesses have to leverage the data they are collecting in order to remain a part of the competition. For years, cost has remained the main reason why small businesses did not adopt big data analytical technologies, but this has changed now. There are budget-friendly tools available for small businesses to take advantage of the data they are collecting today. According to some experts, small businesses can take better advantage of big data since they are able to make the necessary changes far more quickly than large enterprises i.e. real-time response to insights from available data.

According to an IDG study in 2016, 78% of the large enterprises agree that big data strategy has the power to change how businesses have always operated. This shows the acceptance of big data technology and strategies for large enterprises and strengthens the fact that small businesses could become irrelevant if they did not adopt the same strategies.

Benefits of Large Data Analytics

Large Data and Big Companies, a report by IIA Director of Research, Tom Davenport, reveals that businesses are benefitting greatly from big data analytics especially in improving their products, making business decisions faster and reducing costs. Here are some ways small businesses can benefit from big data.

• Cost Savings

The upfront cost of implementing large data tools and strategies is high undoubtedly, but the long term benefits of doing so are matchless. The healthcare industry is a great example of how the use of large data can help businesses reduce their costs regardless of their size. Using predictive analytics, Medicare and Medicaid Services forestalled more than $210 million fraud in healthcare with just 2 years of utilizing the aforementioned technology.

In addition to preventing frauds, small businesses can also reduce their costs by steering clear of creating more inventory than needed, including better partners in the supply chain, etc.

• Improved Decision Making

This is the biggest benefit of large data. It allows businesses to expedite decision making by processing the data fast and giving insights in timely manner. In the past, business decisions were reserved for future strategies in the light of available data and the trends that were observed in it. The amount of data that businesses are sitting on today is huge and thus more powerful insights are to be gained from it.

This vast amount of data requires the use of modern big data hardware technologies. Once both the things are in place for a business, they can understand customers better, create products that are a closer reflection of what customers want and develop a brand based on most reverenced values.

• Impenetrable Security

Businesses are more focused today on using large data to improve their core capabilities, but less attended to a more serious concerns i.e. cyber threats and security breaches. Almost every small, medium and large business is connected to the internet today. Moreover, the Internet of Things has broadened the attackable security surface for businesses making it easier for cyber criminals to attack networks and penetrate company databases.

The biggest concern for modern businesses is that they have to “respond” to cyber attacks that have already occurred rather than being able to prevent them from occurring. The use of big data and big data analytical tools can be a game changer in this area allowing businesses to prevent any security attacks way before they are executed.

Key Technologies Helping Businesses Make the Best of Big Data

As a small business, you will have to familiarize with various technologies that help you store, analyze and take action on big data. Here are some important ones:

• Hadoop

It’s a framework supporting the storing of large amounts of data using an open-source approach. Once data is stored, Hadoop enables the use of a variety of applications using “clustered hardware” at its base. Due to the cluster of commodity hardware, it becomes possible and easy for businesses to process big data despite its increasing volume.

• Data Mining

This is the technology that enables business owners to capture insights, patterns and trends from big data that otherwise cannot be obtained with a traditional approach. Data mining is what allows you to jump in a large sea of structured and unstructured data, make sense of it all and produce analytical insights that help businesses with real-time and future decisions.

• Predictive Analytics

Predictive analytics walk side by side with data mining. Artificial is at the heart of predictive analytics, enabling business owners to adjust their present strategies by giving predictions about what will transpire for the business based on existing data.

• Text Analysis and Mining

One huge concern for modern business is to analyze the noise on various web platforms such as forums, blogs, social networks, etc. to understand what customers expect from a particular brand. Finding out about brand mentions and understanding customer’s impression of your brand can help you readjust your marketing approach to gain customer loyalty. Text mining helps small to large sized businesses go through text data from emails, blog posts, social networks, etc. and hear customers “voice”.

With the rise of smartphones, artificial intelligence and IoT (internet of things) it has become clearly visible that businesses will now have to handle mountains and mountains of data, and make use of big data analytical and processing technologies to have the competitive edge.

If you want to know who visits your site, what they do while they are on their site and why they leave, then you need to be concerned with web analytics. Web analytics are essential if you want to find out information about people who visit your site. Web analytics can also help you determine which sales pages are performing well and which ones are not. Lastly, web analytics help you determine which keywords are driving the most traffic to your site. There are many benefits for web analytics but below are the top 5 benefits.

Benefit #1 – Traffic Count

Have you seen counters on the bottom of web pages that shows how many visitors have come to your site? The good thing about web analytics is that you can get much more precise information. You can breakdown your visitors by how many are new visitors and how many are repeat visitors. You can also determine what pages they have viewed from your website.

Benefit #2 – Individual Visits

Now that you know how many visitors have come to your site and whether they are new or repeat visitors, you can gather information about individuals that visit your website. For each person that visits your website, you can find out what was the first page they visited and what was the last page they visited prior to leaving your site.

Benefit #3 – Total Time

Now that we have a good idea of the visitors to our site, you would want to know the total time visitors are spending on your site as well as the path they took through your site. You can use this information to determine how people navigate through your site.

Benefit #4 – Visitors

The fourth benefit of web analytics is that you can determine the links that brought your visitors to your site. Visitors either come to your site from some other link or they come directly to your site.

Benefit #5 – Keywords

Lastly and most importantly is determining the keywords that people are using to get to your website. If you determine that visitors are using particular keywords to find your site, then you want to optimize your site for those keywords. This is great opportunity to optimize your content to what your visitors are looking for when they visit your site.

As you can see, there are many benefits of web analytics. Ultimately, the benefit of Web Analytics is to make your site more relevant for your visitors.

The key differentiator between two startups is pace. Things need to be done at a faster pace for startups to be competitive against large companies. And, in order to react to market conditions and changing consumer trends, startups today rely heavily on data analytics. The power of being able to gather, identify, understand and execute upon patterns of data is critical for long-term success of companies as well as for advancement of humanity.

Any organization can leverage the exponential data growth but size is on the side of smaller businesses that are perfectly qualified to act on data-derived insights with speed and efficiency, unlike large organizations that are often less nimble and hindered by clunky, legacy IT infrastructure. All that's required is someone in the business that understands two key fundamentals: data analytics and data science.

For example, for a startup organization, product marketing act as a growth catalyst in establishing brand value in the market, which is very expensive and usually eats up a huge part of the budget.

However, while a business can be built on a combination of inspiration and perspiration, being able to manage analyzes and interpret data requires a very specific skill set that will actually enable innovation and drive it forward. From predicting and reducing churn to winning business from new and existing customers, the opportunities are endless.

Data Analytics can help startups in identifying and reaching out the right target market for launching product (s) and providing better return on the marketing investments. Moreover, it can also help in understanding the customer needs and leveraging their requirements for designing or updating offers.

Advertising and marketing without data based insight are akin to trying to hit a target in an unfamiliar dark room with only 2 to 3 bullets in your gun. While Big Data science is evolving, and is not fully accurate, it does tell you the direction in which to shoot, so that your probability of hitting the target is higher.

Regardless of you are looking for funding, thinking about the best way to deploy your latest round of investment or a scale up looking to fuel growth, here's five quick ways analytics and data science can help you:

Evidence-based decision making: One of the rarest commodities when a business is in the growth stages is time. Decisions are taken in days, sometimes hours that in more established organizations would take months. Young businesses especially spend most of their early stage time advancing the market and looking for the right product offering to execute upon. Unlike an established company, one mistake can cost its future so having a data scientist on board is the key to being able to gather and analyzes data from multiple channels to mitigate risk and improve decision making.

Test your decisions: Making decisions and implementing change is only half of the battle; it's vital to know how those changes affect the company. A data scientist can measure key metrics related to important changes and quantify their success (or lack of them) so that lessons are made and substantiated when it comes to playing back results to investors and moving the business forward.

Perfecting the target audience: Everything from social media profiles to website visitor reports contains data which can help a startup pinpoint its target audience – and therefore target them more effectively. Even if it has gone as far as rough identifying its demographics, a data scientist can identify key groups with laser precision through careful analysis of disparate data sources. This in-depth knowledge can help tailor products and services to key customer groups.

Making use of the information: Data has to be at the fingertips of every decision-maker, which are usually most people in the business at its early-stage. This is reflected in the data science and analytics space right now with predictive modeling and machine learning both appealing huge amounts of interest – a sentiment underlined by the recent acquisitions of DeepMind. It is not hard to see why when this particular type of data management enables real-time responsiveness when it comes to translating the raw data into insights, which can be transformed into actionable applications to propel business growth.

Attract the best talent: With a wealth of information on the talent available to businesses today, a data science or an analytics specialist can hunt out the candidates who fit best with a company's needs. Through data mining the vast amount of data talent already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help recruitment teams make speedier and more accurate selections saving money in both short and long term.