How Freelancers are revolutionizing Data Science Consulting?\nWe are living in a world where data gets generated faster, in larger volumes, from countless sources, having numerous formats, and is drastically varied in their structured-ness. Performing analytics on such data for making intelligent business decisions cannot be done solely by the generalists you have working for you. Quite naturally, the skillset required for a data scientist’s role has moved beyond asking for one set of generalists, (read in-house team) addressing all the functional domains and possessing “an analytical mind having expertise on certain BI and reporting tools”.\nEnter the Gig economy for Data Sciences…\nSpecialism – The Missing Virtue\n
Having data science deliver meaningful results requires much more than what most organizations have in-house. The missing element is specialization, which nearly necessitates that you hire experts – freelancers and boutique firms who have graduated from “being a generalist” to “being a specialist” that breathes data science. These data mercenaries in their previous avatars have been mathematicians, statisticians, computer scientists and through rigorous experience in a domain have earned an expertise in building data models and the intelligent algorithms refined over years for varied analytics purposes.\nIn other words, these freelance, domain specific data scientists have a naturally analytical mind, sharpened with their years of statistical training, knowledge of deep/machine learning & AI, along with their expertise on various tools, programming platforms, and algorithms constituting the data science ecosystem.\nWhat Distinguishes an Ideal Freelance Data Scientist from hiring Data Science Service Companies or Leaning on an in-house team?\nFreelancers are available with more specific domain expertise, some of them can be an expert on IoT sensors, and others can be an expert statistician. So, these freelancers can be chosen wisely as per the business requirements.\nThe freelancers are available for shorter project tenures as well as longer tenures unlike Data Science Companies, who majorly are willing to work for longer tenures under retainership based structures.\nOne of the hidden byproducts of hiring freelance data science experts is immediate and swift delivery. These data science experts can help you squeeze more opportunities from the existing data systems coherent with the growth of your business. Freelancers are always motivated to perform an excellent job with quick turnaround time. They fully understand that it’s to their best interest when they remain reliable and exceed your expectations.\nIt is rightly said “A penny saved is a penny earned.” These freelancers are generally available at more affordable costs in contrast with hiring Data science companies.\nCogniticx – A Talent Pool of Data Scientists\nFortunately, hiring a data scientist doesn’t have to be hard or too expensive. Cogniticx helps you realize the value of your data, regardless of its source, type and complexity, to ensure you make intelligent decisions. Cogniticx curates the best talents and assists you with their analytics services at an optimal cost, ensuring complete protection for your interests and most importantly, data privacy.

Gig-fying Data Sciences

That Big Data and Analytics are potential game changers almost sounds like so last decade.   Such wide ranging has been their acceptance that most organizations, across industries, are scurrying to get on to the bandwagon, lest they should miss out on the plethora of likely benefits.\nScores of corporations of all sizes, from Banking & Financial Services to Discreet & Process Manufacturers, from Telecom operators to Healthcare providers, from Retail & Ecommerce to Public Sector Enterprises & Utilities all recognize the opportunity to save costs, increase revenue, improve bottom-line and delight the customers.\nHarnessing the power of Analytics tops boardroom agendas and increasingly the operational teams are tasked with putting together a PTE – detailed Plan to Execute.\nThe classical approaches, albeit often found wanting, to leverage the Power of Analytics, include one of the two choices, akin to the traditional make or buy decisions. The former includes setting up an Analytics COE – complete with cutting edge technology and in-house Data Scientists under the aegis of the CIO or the newly carved out Chief Analytics Officer. The latter comprises approaching a Data Service Provider and outsourcing bulk of the work.\nWhile one could argue from both sides as to the efficacy of the either of the Classical approaches, there is a new paradigm emerging over the horizon that combines best of both the worlds. I call this the \n“Uberization of Analytics”\n.\nThe end game for most organizations exploring either of the Classical approaches is an \nOptimized Decision\n. However, in pursuit of this scarce commodity termed Optimized Decision, organizations end up spending millions on Technology Platforms and on recruiting the best number crunchers. These Technology Platforms are woefully prone to obsolescence and the talent needs frequent upskilling, i.e. if you manage to find the right mix first up.\nThe \nUberization of Analytics\n brings together the best talent from a global pool of resources unconstrained by geography and the limited need to invest in Technology platforms. All this \nOn- Demand\n whenever an Optimized Decision is required!\nIn addition is a huge advantage of having hyper-specialized resources working on hypercritical projects every time that an Optimized Decision is needed. There is no Technological glut and neither are you stuck with resources that are under skilled for a particular task!\nThe emerging paradigm brings in its fold a tremendous promise for late and early adopters alike. The case for \nUberization of Analytics\n has never been stronger in pursuit of the \nOptimized Decision\n.

The Uberization of Analytics

\n\nAll global crises, be it the economic ones such as the Great Depression of 1930, the Financial Crisis of 2008 or the health ones such as the Spanish Flu of 1918 or the more recent SARS, Nipah, Zika viruses have brought out the frailties of globally integrated systems.  While, the cohesiveness of global systems has led to tremendous operational and cost benefits, it has also ensured that one negative impetus, however insignificant, leads to a crippling Domino effect, one that has the potential to shake the very foundations of the system.\nAs one of the most paralyzing health crisis engulfs our planet and country after country grapples with the grim reality, the magnitude of havoc that Covid 19 manages to wreak on the globally integrated systems will only be known as and when the dust settles. The experts are scrambling to ascertain the overall impact of Corona Virus and there are simulations ranging from an optimistic minor contraction of the global GDP to a pessimistic full-scale contraction similar to the one witnessed during the financial meltdown of 2008-09.\nOur new enemy, all of less than 0.2 microns in size, ironically crowns over us in the interim (etymologically Corona is Latin for Crown). No doubt the humans shall prevail eventually, and the world will emerge stronger from the crisis, as it has in the past. To do that it’ll have to learn its lessons quickly and ensure the learnings are enduring.\nChanging Nature of Work in Data Science – The Gig Imperative\nThe global order that emerges, however sketchy it may appear as of now, will have to incorporate strategies for Workforce Resilience. Scores of large and small enterprises alike have been compelled to rethink their talent strategies, due in large parts to the voluntary or mandatory lockdowns. As some customers fret over deliveries and others scale down their projections the demand side has taken a major beating and technology teams across industries are facing the following dichotomy:\nThere is excess staff on the bench without gainful work.\nThere is a lack of adequately skilled staff to deliver as the teams have been rendered out of action due to the lockdowns.\nThe Laggards and the Out-Runners\nAs organizations react to this uncertainty, the tactics mandated by spiraling operational costs would range from pay cuts to large scale layoffs in order to preserve cash. Paradoxically, the skills that organizations-built overtime will unravel. All at significant long-term costs!\nLaggards\n – Once we do get to the end of the tunnel the Laggard organizations that don’t learn fast enough would be led to another reverse scramble to rebuild teams. These organizations would once again build-up costs and wait for the next crisis to react to!\nOut-Runners – \nContrastingly, the ones that’ll out-run and out-smart are the ones that’ll invest in a new people strategy, one that effectively leverages the potential of the Gig Economy. Specialist task teams for every assignment, staffed with skills handpicked to address a particular business problem, assembled on demand and near real-time.\nBenefits of a Gig Staffing Strategy\nSuch a strategy would ensure that the best Data Science talent is put on the job for every assignment, leading to successful quality deliveries.\nThe AI talent pool could be scaled up or down, aligned with the business priorities\nIt’ll level the playing field for smaller companies that often get out-paid by larger competitors in the fight for AI talent.\nIn times of crisis, such as the one that the world faces currently,\nOrganizations are ensured there is a steady pipeline of talent available to work remotely, better insulated by lockdowns\nOrganizations are not saddled with excess layers of flab and costs that need to be shed at a drastic rate in order to steady the ship.\nHow does one leverage the Gig-Strategy in Data Science\nA good starting point is to get a discussion started with a Gig Platform. The engagement may involve an interlock with the Platform to align for short, medium and long term staffing priorities. The Platform may offer a one-stop shop for all Data Science talent requirements and help scale up and down on demand.\nOut-Runners\n will increasingly lean on a hybrid talent strategy pivoted around the flexibility that a Gig Platform offers. That, expectantly, may just be the missing piece in the talent jigsaw that organizations find themselves grappling with in times of crises!

What the Corona Crisis will Teach Us, Eventually! – The Gig Imperative

Analytics has been of paramount importance in the modern day and age. If data is the fuel, analytics is the medium by which it gets converted into energy. Analytics is the force behind converting data into intelligible and meaningful information. Though most organisations believe in investing on resources and state-of-the-art technology, successful analytics may usually just need intervention to capitalise maximum on your data.\nHere are the most important steps you can follow to plan and execute your analytics strategy:\nDefine your goal (mission and vision): \nAs an organisation, it is important that you know where your destination is, having well defined goals will help you find the best way to achieve them. It also helps align the organisation culture, work-force, milestones in a way that success can be achieved with optimal use of resources.\nAssess your current state:\n Most organisations have a fragmented approach to begin with, organisations usually follow through various stages of maturity to reach an integrated, automated advanced model of analytics where there is a seamless and most accurate transformation of the available data into given information. Evaluation of your current situation is important as it helps identify the deficiencies and bridge the gap to improve your capabilities.\nAddress your current incapability:\n After a thorough assessment you can get a holistic idea of the currents deficiencies in your organisation. It could be skills mismatch, culture issue, inefficient flow of information across teams, or a combination of all the problems. These problems can be tackled by:\nSourcing data efficiently\n: Usually companies are furnished with all the data they could possibly need, however inefficient utilisation of data by employees and managers can be a hindrance while taking business decisions. With digital media, social platforms there is a further seamless flow of data, organisations must employ creative ways to fetch data, use them appropriately to take well informed decisions.\nDeveloping better business models:\n Better models help transform data into meaningful information that can aide businesses in taking better decisions. Creating efficient, practical models that aren’t very complicated will help your data-analytics endeavours by a great deal.\nDefine your potential opportunities: \nOnce the business deficiencies are handled, it is best to start focussing on your potential opportunities, the target market you are aiming to crack, and the actions needed to put your plan in motion. This can be done in a several ways:\nBy recruiting leaders or staff across teams to nominate projects that can be considered.\nBy building IT technology to refine initiatives aligned with goals, cost-effectiveness, success rate, etc.\nBy establishing smaller milestones in lieu of bigger successes in the future.\nDevelop platforms to encourage investments in foundational capabilities.\nExecute, monitor and rewire: \nThe best analytical capabilities are usually reliable, reusable and responsive. With changing business needs, your organisational structure (human and processes) must be adaptive to change. With modern BI capabilities aligned to collaborate, adapt and deliver in time, organisations are now focussing on maximising business value, accommodating changing needs and delivering in predicted time and cost. Thus, fine-tuning according to prevalent business needs should be an important aspect while planning your organisation’s analytical endeavours.\nThus, navigating through your businesses growth metrics could be challenging, but an optimal combination of skilled workforce and efficient IT infrastructure can help you strategically grow into a successful enterprise.

Guessing the Right Door! Right-Strategize your way to Success in Analytics….

The definition of Artificial Intelligence has changed drastically over the decades from being described as any task that has been performed by a program or a machine, AI systems have typically emerged to immulate some or many behaviours demonstrated by the human intelligence like planning, learning, problem solving and reasoning, perception, motion, manipulation and are now progressing towards creativity and social activities. With tremendous potential, AI can prove instrumental in creating new employment opportunities, it can improve the quality of life by automating tasks.\nSome noteworthy emerging trends in AI to look out for are:\nThe future for data-scientists\n: Tech giant IBM predicts that the rise in AI will create a huge demand for data-scientists, as machine- learning AI systems use probability for determining an answer/ decision for a given problem. More data will help these machines make better predictions. Companies will hence strive in further mining and analysing data, thereby increasing the demand for data-scientists.\nContent management with AI\n: With global giants like Wibbitz offering SaaS (Software as a service) platform to convert written content into video, and CBS using AI to develop content; this trend to use AI to generate more media is only natural.\nPredictive Analysis\n: It is a known fact that Facebook uses statistical modelling to assist machines in making informed decisions regarding what should appear on your page next. This is computed based on your latest interests, search engine results, geographic profiling, relationship status to name a few. Peer-to-peer networking will further help in having even small and medium scale industries harness this collective power using advanced AI programs.\nSmart living\n: With the reception to devices like Alexa, Google Home, Apple Airpod it is evident that smart assistants using voice technology are popular. This will further surge the use of these assistants to be integrated with mobile devices, computers and even home appliances.\nSelf-driving automobiles\n: Tesla was the first to launch a self-driven vehicle having automakers like Audi release their own counterparts of the same. As reported, Goldman Sachs expects self-driven vehicles will cause 25,000 truckers to lose their jobs, however this is also envisioned to reduce accidents, manage traffic bottlenecks and generally improve the quality of life.\nSmooth sailing Logistics\n: Amazon Robotics aims at providing warehousing solutions to minimise human intervention. Warehouses in the future will be deigned to accommodate robots that can function 24/7 even without lights.\nRobots for natural calamities\n: Developed economies are now computing technologies along with robotics to help create robots that could work in disaster management when stricken by natural or man-made calamities. Atlas was one such example of a humanoid robot developed by Boston Dynamics.\nMeaning to unstructured data\n: AI systems help bring structure to random data. In partnership with Big Data, these systems have helped create meaningful insights. Travel industry has been immensely benefited with it. The tour packages, discounts, seasonal offers are suggested based on these insights. Further personalisation for optimum customer satisfaction is expected in the future.\nGlobalisation in a click\n: With Booking.com translating content in 43 different languages to give customers a personalised experience. It is just a matter of time till more and more companies make user experience across the globe better. Thus, AI systems will help make the world one global village\nThese trends show that in the future businesses would be majorly operated with the help of AI systems. From self-driven cars to automated check-ins at airports and hotels, AI empowered with insightful data mined by genius data-scientists will enable the world to operate smoothly.\n

How to Build a Perfect World Predictions for the Future and the Impact of Artificial Intelligence

The \nShared World\n espouses an economic model based on a peer-to-peer activity of selling, buying, or sharing access to goods and services facilitated by an \nOnline Platform\n.  From flight tickets to a ride, from finding a technician to booking a hotel to ordering groceries, you can get practically anything online by connecting to someone who offers what you need. The possibilities in a sharing economy are endless and it would not have been possible without \nData Science\n and its ability to bring us together.\nHow does Sharing Economy Work?\nSharing economy simply relies on the concept of having a community based platform facilitating transactions between a pair of individuals, it ends up creating a ‘win-win’ situation for all the parties involved. For instance, AirBnB allows you to act as a hotelier by helping you monetize your unused living space. On the other hand, it allows a guest to find a great place to stay at a competitive price with an added warmth of being welcome at a home. There are numerous similar organizations that are leveraging sharing economy for offering services and creating a win-win situation for all the participants.\nHow Data Science Benefits Each Participant in a Sharing Economy\nIn a typical sharing economy model, there are three parties involved:\nThe consumer\nThe provider (of services or resources)\nThe community platform\nThe provider has services, products or resources that he/she can offer to others for a price but that requires a platform where such offerings can be showcased. The community platform (such as Uber or AirBnB) provides them a place where they could advertise their offerings. In turn, the consumer can view, select and pay for the services/resources they would like to buy.\nLet us see how data sciences help such a transaction:\nFor the Consumer\nBig data analytics take into account your preferences, location, payment patterns, buying history, social connections and many other such parameters to give you the most personalized search results. It also analyses the attributes of the offering, the provider’s ratings and preferences in order to maximize the relevance of the search results. This helps you choose the best option without spending too much time looking at the less relevant options. Also, the analytics driven reports & dashboards help you compare various providers statistically in a way that helps you make the best decision.\nFor the Provider\nA provider benefits by being able to reach their most likely customers, thanks to the profiling and segmentation enabled by data analytics. In addition, based on the analysis of various parameters, providers are able to apply intelligent pricing schemes & promotions etc. in order to boost their profitability. Also, because of an increased relevance of the consumers to the offering, chances of gaining better reputation also increases for a provider. Of course, various kinds of reports & predictions help the providers to make tactical & strategic decisions as well.\nFor the Community Platform\nA community platform’s success is directly proportional to the trust instilled in them by the consumers and the providers. With the help of fraud analytics, risk scoring, fake reviews detection,  customer profiling and analysis of their buying propensities, the platforms minimize the risk for the consumers as well as the providers.  In addition, analytics help them connect the right people with the right counterparties, which results in an increased revenue by the community platform.\nIncrease Demand for Data Scientists\nEach of the companies hosting sharing economy rely on the manipulation of data and algorithms to bring together the service providers and customer. For this, they need a team of data scientists, data science experts, data specialists and software engineers making the demand for data science experts at an all-time high.\nSo what does that mean for the future – community Platforms for Data Sciences?\nThe explosive growth of companies powered by the sharing economy makes it amply clear that sharing is the way forward and it is here to stay and grow. Not only start-ups but also larger players are increasingly embracing it because they foresee the benefits that come along. This means that Data Science skills supply shall continue to trail its demand by an order of magnitude.\nIn the wake of the above, Data Sciences community platforms such as Cogniticx shall continue to play a stellar role in bridging this gap by helping organizations hire analytics professionals, on Demand. Cogniticx leverages sophisticated algorithms while matching exceptional Data Science talent with unique client requirements. In the process helping clients hire analytics professionals while ensuring service providers get remunerated for their effort.

Data Science leads the way towards a Shared World!

Over the last decade there has been a tremendous increase in understanding and cognizance of utilising data science capabilities to optimise business outcomes, achieve competitive advantage and increase profitability. However, developing in-house employees to be stellar data-scientists through skills training is often seen as time consuming, inefficient resource allocation and a burden on the costs by organisations. Hence many companies are leaning towards outsourcing these competencies to analytics vendors.\nThough traditional models of data-science outsourcing were popular, their inability to maximise value and mitigate risks for the buyer has led to businesses desiring for mature or alternate models. With enterprises being subjected to greater global economic uncertainty, fierce competition, constant disruption in technology, the desire to have more dynamic liaisons with a guarantee of predictable return on investment (ROI) is now more than ever. Outcome based delivery model is a result of these expectations, which enables buyers and providers to collaborate, achieve optimum and measurable returns.\nCertain advantages of outcome-based delivery models in comparison with traditional models are:\nMinimising the delivery risk:\nIn outcome-based models, since the analytics service provider takes responsibility for delivering measurable outcomes or pre-defined outputs, they are more aligned to take ownership of the end results.\nFor e.g. If a vendor is approached to optimise store check-out times, in traditional model vendors would be inclined to analyse the workloads, timelines, etc. However, in outcome-based models the vendor would use analytics to trouble-shoot around processes and technology to reduce the check-out time.\nBetter value for money:\nIn many cases outcome-based models work on the principle of revenues generated or cost-savings achieved. This helps vendors optimising resources to get best returns.\nA good example of this arrangement could be the Air-Audit solution designed by Happiest Minds. It is an outcome-based pricing model where the airlines pay a percentage of revenue leakage identified by the solution. This solution is designed to prevent unnecessary Global Distribution System (GDS) related costs and inventory spoilage caused by non-compliant booking practices.\nCustomisation based on individual needs:\nUnlike traditional models that follow regular practices (like staff augmentation, product-based services) to achieve linear growth, outcome-based models help optimise results.\nA note-worthy example in this regard is how big-data has enabled farmers achieve better results based on their individual needs, thus converting the agri-business into a specialised serviced based industry, rather than a product based one. A collaborative effort in this regard from all allied industries will help the overall crop-output. For example: the fertiliser industry can provide fertilisers based on historical analyses of the soil sample, thereby optimising the cost, and accentuating soil quality.\nLean approach to maximise profits:\nAs all the stakeholders share a common goal of achieving the pre-decided outcome, there is further focus on the exponential growth to be achieved. The outcome-based delivery model typically works with vendors deploying their most skilled resources to the project, who encourage innovation in processes, thereby creating an agile work environment.\nTransparency:\nAs the outcome-based delivery model works as a win- win situation for the vendor and the client, there is an absolute transparency, which further helps build long term relations between both the parties. This in a way ensures client’s data is protected from being shared beyond the scope of work or contract between the stakeholders.\nIn summary, outcome-based delivery model is a “Value driven paradigm” that can enable businesses to capitalize on the unstoppable digital transformation the future holds. Unearthing the processes involved in an outcome-based approach will help businesses uncover unexpected sources of growth, and once successfully implemented these can be scaled to greater business outcomes even though requirements may keep changing.

Something’s Wrong in Traditional Data-Science Consulting: The Imperative of Outcomes Based Pricing

Strategies for small and medium businesses to benefit from data sciences, at the lowest cost!\nOver the past few years, there have been quite a few instances of small to medium companies have been able to streamline their operations, introduce efficiencies and reduce costs by integrating data science into their decision making. However, a look at several recent surveys and reports shows that largely companies are still unable to utilize their data as effectively as they should. Even though there is an appetite among top management for using data in a more effective manner, a right data sciences strategy or the lack of it continues to pose significant challenges for most.\nThese challenges limit the organizations’ ability to leverage the right skills, tools & technologies to accelerate growth and are largely related to the following:\nData Availability & Quality\nThe first and foremost challenge lies with acquiring usable data that could be leveraged without much effort spent in improving its quality. In order to obtain the data, the user touchpoints should be designed to enable data collection. Appropriate design, data quality, compliance & storage considerations should be factored in while doing so. This may imply a need for redefining some existing business processes and re-building or modifying the technology components involved in data collection, cleansing, storage, and processing.
To mitigate this small to medium businesses today are looking at service providers who could provide them with actionable insights by leveraging whatever data assets they possess. Data-preneurs with flexi models are the need of the hour.\nCost of Technology Acquisition\nTo support the data science efforts, it requires significant investment related to multiple technical aspects, including network, user touchpoints (such as mobile apps, web, wearables, IoT devices & sensors), data cleansing tools, data storage hardware and software, analytics & reporting tools, to name a few. In addition, the cost of design and implementation adds to the overall cost of technology acquisition.\nGiven the above cost deterrent, it becomes absolutely imperative for small to medium businesses to look at a technology agnostic way of getting to the most optimal decision. Businesses just are concerned with the getting to the best results in a least cost way, no matter the technology.\nInability to Acquire the Rightly Skilled Resources\nIn addition to the investment in the technology acquisition, other vital element is the availability of the right skills. Building an in-house team of the adequately skilled people often turns out to be a long-drawn and expensive affair, which doesn’t guarantee results either. A viable solution to address the challenges in acquiring the skilled people is to hire freelancers having expertise in data sciences. You can use the CogniticX platform to connect with the real experts to mitigate such a risk.\nRight skilling with each incremental functional requirement is a wishful thought. As a result, organizations may be saddled with skills obsolescence, ones that were expensive to acquire and retain in the first place. Further the same resources may lack functional understanding, which could be a critical differentiator. Circumventing the skills obsolescence challenge is a major hurdle in the successful adoption of data sciences.\nData security and privacy issues\nWhile collecting data is vital to perform data analytics, it also opens your organization to the risks related to data theft, violation of data protection compliance and security attacks. This is why it’s very important to consider data privacy and security as one of the supreme requirements and enforce the required data security measures. These measures should be applied at various levels, including physical & online access to data storage, protection of data in motion, secure coding practices and adherence to the industry-specific compliance.\nThe Mitigation\nFortunately, there are mitigation methods available for small to medium businesses to navigate their way around the data sciences conundrum. The foremost is to look at flexi-leverage models such as those offered by Cogniticx, wherein one could engage a data scientist, armed with the right open source technology and skilled with the appropriate domain understanding to help an organization get to the holy grail, the most optimal decision, on demand.

Surviving the Leap of Faith

Do you have it in you?\nIf you are a data scientist keen on juicing enormous amounts of data to build a better tomorrow, you are at the right place. This article will help you embark on your journey to become a successful data-preneur. Let us begin with understanding the skills and traits you need to get started.\nMust-haves Skills/Traits for a data-preneur\nTools and Coding Skills\nIn order to succeed in your projects, you need to have the right tools and coding skills on the right platforms/languages under your belt. For instance, a sample set of skills you should possess may be:\nProgramming languages, such as R, Python or SAS\nDatabase skills on RDBMS (e.g. MySQL, Oracle, MSSQL Server) & NoSQL DB’s (e.g. MongoDB, CouchBase)\nBig Data & ETL tools, such as Hadoop Mahout stack etc.\nReusable Frameworks or Components\nYour speed of delivery brings you an edge over the competitors. To this end, your personal library of reusable components & framework can shorten the lifespan of a project, help the client get a quicker delivery and in turn, earns you the reputation as an expert.\nTheoretical Subject-Matter Expertise\nThis represents your skills related to:\nConceptual understanding of statistics, pattern recognition, Machine Learning, NLP, AI etc.\nAbility to convert mathematical concepts & algorithms into working programs\nDomain-specific experience\nNot essential to begin with, but a huge plus if you have in-depth expertise in few business domains. This will narrow down your marketing focus and help you build a niche offering for yourselves.\nCommunication & Networking Skills\nTo grow any business, access to the right people and your ability to carry out the right conversations with them are two vital elements. Whereas your communication and networking skills are the crucial traits, platforms like Cogniticx will help you reach your prospects in a much easier way.\nCreativity\nAs a data-preneur, you need to exhibit creativity in several aspects of your work. Whether it’s about creating winning sales pitches, explaining your proposals, or creating the most useful reporting dashboards to help generate the desired business insight, your creativity plays an important role at every step of your journey.\nWhat Do You Need Beyond the Skills?\nA polished & rehearsed sales pitch, a proposal template (customizable), your rate card, case studies demonstrating your experience in sufficient detail, and a can-do attitude to win the difficult prospects\nA business model, describing the constituent People, Processes, Technologies along with the projected expenses and revenues. Of course, the revenue model is a vital element as well.\nCogniticx – Don’t Worry, We’ve Got Your Back!\nCogniticx\n helps you act as the starting point to your journey. Cogniticx is a platform built for data-preneurs and it connects the prospects with the most relevant data-preneurs and facilitates the end-to-end transactions, delivering a fulfilling experience for the client and the data-preneur, both.

Imperatives for a Successful Data-preneur

The Big Data Analytics industry has grown by leaps and bounds over the years as more business enterprises recognise the need to have a systematic approach to scale their operations to greater heights in all aspects ranging from Marketing, Operations, Risk, Fraud and Retail to name a few. Additionally, Artificial Intelligence (AI) and Machine Learning (ML) are feeding off the increasing sophistication in Data Sciences and Computer Hardware.\nCogniticx- Where “You” are at the core of our service\nGlobalisation not only calls for an integration of markets but also requires organisations to make huge investments in analytics, both applications and resources,  which can aid them in making strategic decisions to steer their venture in the right direction.\nCogniticx is the 1st and only platform in India that offer an exclusive pathway to bridge this gap and offer our clients a top-notch service for them to develop their analytical enterprise without incurring an overhead cost by providing them with an array of Cogniticx experts tailor made to suit their business requirements.\nMake us a part of your success story\nWhile our aim is to serve our clients and help them flourish in their business, it is also our mission to embrace the diversified talents of all the data scientists widespread across the country.\nShould you be a full-time professional, a freelancer, a student looking to create an avenue to leverage upon your analytical skills, expertise and are looking to build your own portfolio of projects; this is just the platform for you to take charge and help us build long lasting relationships with our clients.\nBe a part of our vision of creating a hub of professional, ambitious and energetic team here at Cogniticx.\nStart your relationship with Cogniticx by signing up on our website and share your preferences with us today!

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