“Enabling clients reimagine their decision making & accentuate the business performance with AI strategy in a transformation, innovation and disruption driven world”

In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture.  Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems.  To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it.  AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.

In the age of the bionic enterprises, AI continues to dominate the technology & business landscape.  Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres.  Traditionally, human decisions are to a large extent based on intuition, gut and historical data.  In the age of AI, several of our decisions will be taken by algorithms.  Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business.  All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage.  If you have not started yet, you are already behind the competition, however large or pedigreed you might be.

So, where are you placed on your AI journey?  At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.

At AIQRATE, we are here to help you with the art of the possible with AI.  Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses.  Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.

We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator.  AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.

Enclosing here our journey walk through of 2020.

Today, most technology aficionados think of data engineering as the capabilities associated with traditional data preparation and data integration including data cleansing, data normalization and standardization, data quality, data enrichment, metadata management and data governance.  But that definition of data engineering is insufficient to derive and drive new sources of society, business and operational value. The Field of Data Engineering brings together data management (data cleansing, quality, integration, enrichment, governance) and data science (machine learning, deep learning, data lakes, cloud) functions and includes standards, systems design and architectures.

There are two critical economic-based principles that will underpin the field of Data Engineering:

Principle #1:  Curated data never depletes, never wears out and can be used an unlimited number of use cases at a near zero marginal cost.

Principle #2:  Data assets appreciate, not depreciate, in value the more that they are used; that is, the more these data assets are used, the more accurate, more reliable, more efficient and safer they become.

There have been significant exponential technology advancements in the past few years ; data engineering is the most topical of them. Burgeoning data velocity , data trajectory , data insertion , data mediation & wrangling , data lakes & cloud security & infrastructure have revolutionized the data engineering stream. Data engineering has reinvented itself from being passive data aggregation tools from BI/DW arena to critical to business function. As unprecedented advancements are slated to occur in the next few years, there is a need for additional focus on data engineering. The foundations of AI acceleration is underpinned by robust data engineering capabilities.

YourStory & AIQRATE curated and unveiled a seminal report on “Data Engineering 4.0: Evolution , Emergence & Possibilities in the next decade.” A first in the area , the report covers a broad spectrum on key drivers of growth for Data Engineering 4.0 and highlights the incremental impact of data engineering in the time to come due to emergence of 5G , Quantum Computing & Cloud Infrastructure. The report also covers a comprehensive section on applications across industry segments of smart cities , autonomous vehicles , smart factories and the ensuing adoption of data engineering capabilities in these segments. Further , it dwells on the significance of incubating data engineering capabilities for deep tech startups for gaining competitive edge and enumerates salient examples of data driven companies in India that are leveraging data engineering prowess . The report also touches upon the data legislation and privacy aspects by proposing certain regulations and suggesting revised ones to ensure end to end protection of individual rights , security & safety of the ecosystem. Data Engineering 4.0 will be an overall trojan horse in the exponential technology landscape and much of the adoption acceleration that AI needs to drive ; will be dependent on the advancements in data engineering area.

Please fill in the below details to download the complete report.

The management of talent has always been and continues to be a major challenge for most industries. This is particularly true for knowledge based industries like information technology. The dramatically changing dynamics of the Indian Technology industry compound the challenges and opportunities faced by the industry.

Never since the advent of mass production has an industry seen such dramatic volatility in such short period of time. The revolution before primarily added to the productivity of the labor and moved across the globe. The current revolution is not merely transcending national borders – it is redefining jobs, eliminating others and creating new opportunities.

Please fill in the below details to download the complete report.

AI = The Future of “H” in HR : Introducing AIQRATE’s consulting offerings for CHRO/CPO/HR leaders

AI = The future of “H” in HR . In today’s competitive businesses , the role of AI in planning, operations & strategy has transformed from being a competitive differentiation to a competitive necessity . The age of “ trust me , this will work” is over. In the current business mandate , where HR is held accountable for delivering business results , it has become imperative to harness the power of AI . AI can elevate HR from a tactical support function to a strategic transformative function . HR business function disruption thru Talent Sciences : business capability of using AI and algorithmic modeling to drive HCM decision making will form the backbone of HR function.

Introducing AIQRATE’s consulting offering for Chief Human Resource Officer (CHRO) / Chief people officer (CPO) / Chief Talent officer (CTO) /HR Leaders working across Enterprises , GCCs , SMBs , Startups , Public Institutions : 

1. AI master class session : Contextualized for CHRO , CPO : demystify AI , AI strategy canvas , AI landscape & wide applications , HR vale chain interventions
2. AI advisor on-demand : Build AI led decision making strategies and processes across the HR value chain and strategic interventions
3. AI talent mapping strategies : Execute AIQRATE “T-REX” framework for building enterprise wise AI skilling & learning regime
4. AI led interventions for CHRO/CPO : Reimagine HR domain , HR business function problems and scenarios leveraging AIQRATE consulting expertise
5. Analytics to AI maturity assessment : Gauge your enterprise AI adoption maturity with AIQRATE “Elevate” transformation journey framework

AIQRATE’s extensive yet bespoke consulting offerings for CHRO/CPO/HR leaders focuses on building AI led strategies on talent workforce decisions and tracking performance of HR strategic initiatives and also on building data driven discovery algorithms on improving HR process efficiencies and outcomes. 

AIQRATE’s attempts to gear up HR leaders to the future of work and our curated offerings will enable navigate four broad shifts for HR leaders :

1. Accentuate strategic business acumen

2. Augment AI driven expertise for decision making

3. Amplify “transformation driven impact “ within the HR business function.

4. Accelerate “innovation driven culture” within the HR team

Reach out to us at consult@aiqrate.ai for detailed view and approach on our extensive AI consulting offerings for CHRO/CPO/HR leaders .

The current boom of exponential technologies of today is causing strong disruption in the talent availability landscape, with traditional, more mechanical roles being wiped out and paving way for huge demand for learning and design thinking based skills and professions. The World Economic Forum said in 2016 that 60% of children entering school today will work in jobs that do not yet exist.

While there is a risk to jobs due to these trends, the good news is that a huge number of new jobs are getting created as well in areas like AI, Machine Learning, Robotic Process Automation (RPA), Blockchain, Cybersecurity, etc. It is clearly a time of career pivot for IT professionals to make sure they are where the growth is.

AI and Machine Learning upending the traditional IT Skill Requirement

AI and Machine Learning will create a new demand for skills to guide its growth and development. These emerging areas of expertise will likely be technical or knowledge-intensive fields. In the near term, the competition for workers in these areas may change how companies focus their talent strategies.

At a time when the demand for data scientists and engineers will grow 39% by 2020, employers are seeking out leaders who can effectively work with technologists to ask the right questions and apply the insight to solve business problems. The business schools are, hence, launching more programs to equip graduates with the skills they need to succeed. Toronto’s Rotman School of Management, for example, last week launched a nine-month program which provides recent college graduates with advanced data management, analytical and communication skills. 

According to the Organization of Economic Cooperation and Development, only 5-10% of labor would be displaced by intelligent automation, and new job creation will offset losses.

The future will increase the value of workers with a strong learning ability and strength in human interaction. On the other hand, today’s highly paid, experienced, and skilled knowledge workers may be at risk of losing their jobs to automation.

Many occupations that might appear to require experience and judgment — such as commodity traders — are being outdone by increasingly sophisticated machine-learning programs capable of quickly teasing subtle patterns out of large volumes of data. If your job involves distracting a patient while delivering an injection, guessing whether a crying baby wants a bottle or a diaper change, or expressing sympathy to calm an irate customer, you needn’t worry that a robot will take your job, at least for the foreseeable future.

Ironically, the best qualities for tomorrow’s worker may be the strengths usually associated with children. Learning has been at the centre of the new revival of AI. But the best learners in the universe, by far, are still human children. At first, it was thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems — the corridas of nerd machismo — would prove to be hardest for computers. In fact, they turn out to be easy. Things every dummy can do like recognizing objects or picking them up are much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby. The emphasis on learning is a key change from previous decades and rounds of automation.

According to Pew Research, 47% of all employment opportunities will be occupied by machines within the next two decades.

What types of skills will be needed to fuel the development of AI over the next several years? These prospects include:

• Ethics: The only clear “new” job category is that of AI ethicist, a role that will manage the risks and liabilities associated with AI, as well as transparency requirements. Such a role might be imagined as a cross between a data scientist and a compliance officer.
• AI Training: Machine learning will require companies to invest in personnel capable of training AI models successfully, and then they must be able to manage their operations, requiring deep expertise in data science and an advanced business degree.
• Internet of Things (IoT): Strong demand is anticipated for individuals to support the emerging IoT, which will require electrical engineering, radio propagation, and network infrastructure skills at a minimum, plus specific skills related to AI and IoT.
• Data Science: Current shortages for data scientists and individuals with skills associated with human/machine parity will likely continue.
• Additional Skill Areas: Related to emerging fields of expertise are a number of specific skills, many of which overlap various fields of expertise. Examples of potentially high-demand skills include modeling, computational intelligence, machine learning, mathematics, psychology, linguistics, and neuroscience.

In addition to its effect on traditional knowledge workers and skilled positions, AI may influence another aspect of the workplace: gender diversity. Men hold 97 percent of the 2.5 million U.S. construction and carpentry jobs. These male workers stand more than a 70 percent chance of being replaced by robotic workers. By contrast, women hold 93 percent of the registered nurse positions. Their risk of obsolescence is vanishingly small: .009 percent.

RPA disrupting the traditional computing jobs significantly

RPA is not true AI. RPA uses traditional computing technology to drive its decisions and responses, but it does this on a scale large and fast enough to roughly mimic the human perspective. AI, on the other hand, applies machine and deep learning capabilities to go beyond massive computing to understand, learn, and advance its competency without human direction or intervention — a truly intelligent capability. RPA is delivering more near-term impact, but the future may be shaped by more advanced applications of true AI.

In 2016, a KPMG study estimated that 100 million global knowledge workers could be affected by robotic process automation by 2025.

The first reaction would be that in the back office and the middle office, all those roles which are currently handling repetitive tasks would become redundant. 47% of all American job functions could be automated within 20 years, according to the Oxford Martin School on Economics in a 2013 report.

Indeed, India’s IT services industry is set to lose 6.4 lakh low-skilled positions to automation by 2021, according to U.S.-based HfS Research. It said this was mainly because there were a large number of non-customer facing roles at the low-skill level in countries like India, with a significant amount of “back office” processing and IT support work likely to be automated and consolidated across a smaller number of workers.

Automation threatens 69% of the jobs in India, while it’s 77% in China, according to a World Bank research.

Job displacement would be the eventual outcome however, there would be several other situations and dimensions which need to be factored. Effective automation with the help of AI should create new roles and new opportunities hitherto not experienced. Those who currently possess traditional programming skills have to rapidly acquire new capabilities in machine learning, develop understanding of RPA and its integration with multiple systems. Unlike traditional IT applications, planning and implementation could be done in small patches in shorter span of time and therefore software developers have to reorient themselves.

For those entering into the workforce for the first time, there would be a demand for talent with traditional programming skills along with the skills for developing RPA frameworks or for customising the frameworks. For those entering the workforce for being part of the business process outsourcing functions, it would be important to develop capability in data interpretation and analysis as increasingly more recruitment at the entry level would be for such skills and not just for their communication or transaction handling skills.

Blockchain – A blue ocean of a New kind of Financial Industry Skillset

A technology as revolutionary as blockchain will undoubtedly have a major impact on the financial services landscape. Many herald blockchain for its potential to demystify the complex financial services industry, while also reducing costs, improving transparency to reduce the regulatory burden on the industry. But despite its potential role as a precursor to extend financial services to the unbanked, many fear that its effect on the industry may have more cons than pros.

30–60% of jobs could be rendered redundant by the simple fact that people are able to share data securely with a common record, using Blockchain

Industries including payments, banking, security and more will all feel the impact of the growing adoption of this technology. Jobs potentially in jeopardy include those involving tasks such as processing and reconciling transactions and verifying documentation. Profit centers that leverage financial inefficiencies will be stressed. Companies will lose their value proposition and a loss of sustainable jobs will follow. The introduction of blockchain to the finance industry is similar to the effect of robotics in manufacturing: change in the way we do things, leading to fewer jobs, is inevitable.

Nevertheless, the nature of such jobs is likely to evolve.  While Blockchain creates an immutable record that is resistant to tampering, fraud may still occur at any stage in the process but will be captured in the record and there easily detected. This is where we can predict new job opportunities. There could be a whole class of professions around encryption and identity protection.

So far, the number of jobs created by the industry appears to exceed the number of available professionals qualified to fill them, but some aren’t satisfied this trend will continue. Still, the study of the potential impact of blockchain tech on jobs has been largely qualitative to date. Aite Group released a report that found the largest employers in the blockchain industry each employ about 100 people.

Organizations across the globe today are grappling with a data deluge and with the increasing reliance on mining data to carve out actionable insights and drive strategic imperatives, the relevance of building the right ecosystem of Analytics professionals is becoming commonplace. Qualified analytics professionals are scarce, though in great demand and generally command higher salaries than the industry normal because of their specialized skills.

For most, analytics is still in the realms of software tools and creating highly visual dashboards/reports/charts etc. But there’s definitely more to it than what meets the eye. Analytics has lot more to than just jazzing up data; it can enable fact-based business decisions based on that data. It primarily means working closely with the business stakeholders to uncover gaps in the business and using the knowledge to work with data appropriately, to come up with useful insights and recommendations the organization can focus on, to increase top-line or rationalize costs at a high level.

And many a times, the general perception about great talent directly correlates to the pedigree of an individual. Most organizations, especially in analytics space, are extra careful about their hiring channels when it comes to onboarding Analytics talent. And more often than not, we are generally biased to absorbing talent which has a strong pedigree credentials (academic excellence, b-school or t-school grade or tier et al) and fall prey to such generalized notions about building great teams. Unfortunately, Analytics is a different ball game altogether and successful career in Analytics has more to do with the underlying fundamental behavior of an individual. It’s an interplay of multidisciplinary skills ranging from mathematics, to statistics, computer science, communication and not to mention the business knowhow. Pedigree may be just a guiding beacon to highlight potential but definitely not a key ingredient to governing success. Let me shed some light on what it takes to build a successful career in analytics:

Intellectual Quotient

Successful people in the analytics industry today have that inquisitiveness and high curiosity attitude ingrained in their natural DNA. For any given situation they are presented with, they can think through and formulate the right set of questions, the “why’s” “what’s” & “how’s” which is key to succeeding in a professional setup. Even before jumping to the data analysis piece, it’s crucial to understand the business problem at hand, crafting out the specifics of the probable solution approaches and most importantly questioning the underlying assumptions being undertaken.

Especially ‘big data’ is more about the questions being put forward than the data itself.  No data can speak for itself unless appropriately questioned.  Success on dealing with ‘big data’ projects requires a thorough understanding of the problem, narrowing down the right questions, getting those answered by SME’s or business experts on right forums, making sure you harness the right amount of data to answer the questions at hand and then eventually communicating the solution to the target audience (which may be clients or the internal stakeholders).

Driven by Numbers

Being accustomed to using mathematical concepts and mathematical tools is commonplace in analytics space.  Mathematics & statistics forms the basic foundation here and if for any reason this word strikes fear in your heart, think again! As you progress your career in Analytics and if you aspire to be truly a Data scientist, few additional skills shall be instrumental to your success: Machine learning, statistical modeling, experiment design, Bayesian inference, Supervised learning: decision trees, random forests, logistic regression Or Unsupervised learning: clustering, dimensionality reduction, Optimization: gradient descent and variants etc. The key aspect to note here is that most of these skills are picked up during the job or as special trainings and not directly linked to an individual’s pedigree. The number-crunching attitude forms the basis here and this is something inherent to an individual irrespective of which institute or academic background they hail from.

Ability to see the Holistic picture

Data here is just a means to an end and behind the scenes there’s a larger business problem at hand being dealt with. Unless there’s absolute clarity on what the client is actually intending to solve, you might end up looking at the wrong place or assimilate wrong pieces of information which may not be of any use. At times, the client isn’t quite sure about the problem they intend to seek answers to which may derail the whole exercise. Getting clarity on what’s the root cause driving actions is crucial.

There may be too many variables under consideration at the same time, but being able to see through clearly and importantly, being able to identify the next steps based on the larger intent is imperative. For instance, if the individual is assigned a problem pertaining to pricing analytics in an FMCG industry, it is very important for them to understand the dynamics between marketing, pricing, sales, promotions etc. work in this industry before. If it’s about evaluating the effectiveness of a marketing campaign for an FMCG product, domain knowledge shall help in narrowing down the key 10 or 100 variables that need thorough consideration from amongst the thousands available at disposition.

Again this ties back to our initial premise of inherent inquisitiveness of an individual to get the right set of questions framed and answered before any detailed analysis begins. Asking the “Why” questions at every juncture may help to uncover the latent objectives which client may not be articulate well in certain cases.

Orientation to Detail

Cognitive “attitude” and willingness to search for deeper knowledge about everything is a common strain running across all successful analytics professional. Though a bird’s eye view is good to have to better understand the larger business problem being tackled but at the same time balancing it against the specifics which need further drill-down is crucial. While dealing with voluminous stacks of structured or unstructured data, it’s easy to lose sight of specifics which be of immense value in crafting a solution to the original problem. Having that “hawk’s eye” to suddenly fish out significant patterns which may be of interest to business is a must have. Visualizing data through various plotting methods (box plots, histograms, correlation matrix et al) can help uncover those meaningful nuggets which the business is interested in.

Ability to Interpret within the Realms of Business Context

End of the day, it’s important to realize that numbers won’t speak for themselves unless the right set of tools/techniques/methodologies are employed to present the data in a consumable form. Numerous tools in the industry today have plethora of features to simplify data interpretation but the understanding of which visualization technique is most suited to give you the right picture, given the data in question and business problem at hand is the prowess of a well-acquainted analytics professional; one who knows his toolbox in & out. In some cases histograms may deem fit to understand the distribution of data and at the same time the box plot may get you a better idea of how the majority of data points are spread across the spectrum, or if there are any outliers. Domain expertise & business knowhow can help leapfrog your analysis to a different level altogether, help interpret the results in the business context, assess usefulness of results, bringing out insights which may not be that obvious to common folks.

Communication and Visualization

You may be a champion in your rarefied field, but you may not succeed as an analytics professional unless you can’t communicate the value of your analysis in simplistic terms, a language which the client or business user understands. Communicating the value to business people and asking the right set of questions on what’s important is table stake. Ability to convince that what you’ve done is viable and will deliver business value is something one should be excelling at.

Umpteen times there are disparate pieces of information which a good analytics professional should be able to connect and able to convey a compelling story which makes sense to the target audience. As an analogy, a leading insurer was observing overall dipping sales and post analysis it came to notice that customer service in certain pockets or geographies has dwindled because of inappropriate handling of customers over certain touchpoints. The analytics team was able mine the sales data for pain points, narrow down upon the areas with stagnant or negative sales growth and also uncover pattern between unsatisfactory customer comments over social channels (FB page, twitter handle etc). Survey results again hinted that certain geographies had observed lack of customer empathy as a major factor impeding lead conversion and high attrition. Sales data, social data and survey results in totality were able to narrow down upon those specific areas of concerns mapped to respective geographies, which now the business could pursue to chart out a customer experience roadmap for targeted geographies & remedial measures to mitigate potential bottlenecks identified.

To sum it all, a pedigree can convey so much so about an individual’s ability to succeed in building a thriving analytics career. It’s more about those innate capabilities, domain/analytics experience one garners on the job and regular trainings which forms the secret sauce to a differentiating career trajectory in analytics.

Numerous studies in the past have shown that an engaged workforce significantly out-produces an unmotivated one. And even a greater number of those studies reinforce the common notion that employee creativity is the key to innovation and eventual customer or client satisfaction. To a great degree, for most industries and organizations, talent is one of the most treasured asset and to an extent a key differentiator in the marketplace. Every company needs creative team, decision makers and visionaries, but on a similar note, it’s equally important to have a motivated workforce who can give their all for the cause day-by-day. As clichéd it may sound, Analytics truly is one of the industries where the average churn or attrition rate has typically been on the higher side (as compared to industry average in general) due to great demand for Analytics experts in the industry across the globe and dearth of skills required. I presume all my readers would concur on the perennial challenge of analytics talent crunch most businesses are grappling with today. And even if you find the so-called right analytics resource that align with your skills requirements, you’re bound to chart out engaging career prospects and develop that talent over what could amount to a decade before that person is achieving optimum results for your company.

Analytics is all about working with an extremely talented & creative set of people, who need constant care and attention from their leaders & mentors.  And undoubtedly, most businesses yearn to maximize output and increase topline, they need to have their team working as effectively as possible. But how should they go about this? A generally accepted phenomenon that happy workers tend to be productive ones – meaning there are clear benefits to keeping people engaged and motivated.

When it comes to job satisfaction, financial rewards may be lower on the list than most people think and as I reflected in my previous posts. Being happy with the job seems to depend more on the intangibles: feeling part of a team and being valued and appreciated consistently outrank money when employees are polled about job satisfaction. It’s all about keeping employees highly engaged & give them the due respect they deserve. Engaged analytics talent learns, grows, displays high leadership quotient, deliver heightened ROI and significantly decrease your organization’s turnover rate. However, keeping a team that approaches work with vigor and passion intact is easier discussed than executed.

Every other company which employs analytics services or is outsourcing it wants to hire and keep the best of the breed talent – how do you stay a step ahead? Clarity on company’s mission that they are driven to lead themselves is all commonplace and is a sanity in Analytics as with every other industry. And if an employee isn’t engaged creativity is not present! So what truly keeps this evolved species engaged enough?

• Where in the Organization your Analytics Talent sits

First & foremost, as leaders, we must acknowledge that Analytics talent is different from rest of the organizations and placing them rightly within the organization is crucial to ensure they are truly able to create the real impact. Bright minds cannot be chained or siloed or put in a bureaucratic hierarchy, lest they tend to lose their sheen and may end up attriting or be sitting ducks stuck up in the usual business stream where their skills are definitely not put to the best of use. Analytics talent should be led by analytic leaders who know this industry in & out; because they are the ones who understand how this talent needs to be groomed & nurtured, and shielded from the political bureaucracies and the analytics leaders should effectively communicate those differences throughout the organization constantly. Separate HR policies, working culture and operating rhythm is required to give these prodigies a conducive environment to deliver their best.  They should acknowledge different processes for them, they should have different technical ladders, different job expectations. It must be thoroughly acknowledged that they have different motivations and is the organization in the capacity to carve out that special niche for thriving Analytics talent. Whether you keep them together in their own close knit group so they can keep their skills sharp with constant interaction with each other, or should they be spread across company’s business units because that is where they must have an impact?

It’s important for your analytics talent to garner complementary skill sets. Obviously, you intend to build a team with eclectic mix of skills instead of having all people who are good at data massaging or modelling or all people who do optimizations or visualizers. Analytics is a broad space and there are umpteen specialties, and piecing together these different puzzle elements is the key to generating impactful business insights. As an analytics leader because you understand the space, you understand these specialties. So to be a good leader of such a talented team, you really have to focus on the individuals on your team and help them succeed.

• A Robust Career Path

Talent always needs a clear future vision on their career trajectory & growth within the organizations with distinctive career paths through career ladders and lattices. A clarity/transparency on roles, career tracks and skill expectations has to be in place to affirm that your analytics professionals have a well thought through career roadmap charted out. Structured capability building, systematic learning and development frameworks could be crucial to ensuring that career architecture plan is laid out appropriately.

• A Rock-solid Training Regime

An effective training and development plan is one of the best ways to convey to your valued employees the commitment and faith you have by investing in them. And, when you consider this investment in the long run with a way higher ROI, the expense of a solid training program suddenly seems measly. With the pace the Analytics industry is maturing, it’s crucial that your talent is in tune with the current needs in the industry, has hands-on experience with the topical tools & technologies and is abreast with the latest and greatest techniques being used today to deliver business impact. Just to substantiate with an example here, R being open-source has tons of pre-built libraries & many more keep getting added to the repository, avoiding duplicity of effort and ensures optimal procedures/techniques are being employed which have proven to be effective & accredited by experts in the wider public analytics community. Smart talent is always hungry for more and it’s imperative for the analytics leaders to keep feeding their talent with all the trending skills/tools and keep their talent’s arsenal up-to-date.

• Empower them

Give your talent the wings of freedom, to innovate, to be creative, basically get out of their way and empower them, give them the requisite tools to deliver. Don’t keep them shielded from the real-world all the time & get them the exposure to be deeply embedded in the C-suite. The senior executives want to make decisions based on data and they trust this talent tremendously. Get them in front of the clients or the C-suite and give them the platform to talk to them directly as to what they are doing. That’s what keeping them there, as they feel that they are in the driver’s seat helping businesses navigate in a highly competitive, relentless environment. Highlight in appropriate forums about their achievements & the impact they have made.

• Business Exposure

Analytics professionals need to be spending a sizable time(between 6-10 years) to industry-specific challenges before they can have the right context  to understand the problem well and know exactly know what’s needed & what not to go about finding a solution. This is among the scariest challenges which majority Analytics leaders across the globe are trying to figure out and still poses a big question mark for most on how to get their analytics talent a business bent of mind. Continuous exposure to the business, ongoing domain-specific trainings are hence an important ingredient to their success & keeping them relevant

• Challenging Work Environment

Throw all sorts of challenges and varied projects at your talent. Be it as far away from their comfort zones & let them drown in it. This is what they truly enjoy doing and they love finding their way out from the middle of nowhere. Organize week long hackathons, competitions & give them stringent timelines to deliver. Give them the opportunity to make the impossible possible. The sense of achievement & gratification after working their way out from challenges unimagined in their wildest dreams is what keeps them going. Let them mingle up with the wider Analytics talent beyond the walls of the organization; let them participate in global competitions organized by Kaggle, 5^th Elephant, VCCircle, Analytics Vidya etc. Motivate them to join interesting discussions pertaining to Analytics online (twitter, blogs, linkedin communities) and make a contribution, be an expert advisor in the area they specialize in.

• Be the “Employer of Choice”

Value your talent, get them the requisite support and developmental environment from mentors & other experienced professional in your Analytics team, pay them well, recognize their contributions, and give them the opportunity to rise up the career ladder. Start collaborating with academic institutions from where you hire, increase interventions, do guest lectures, seminars, workshops, mentoring sessions, case competitions, “Shadow a Leader” program etc to be constantly engaged with your pipeline talent.

With the recent massive explosion of data availability, significant leap in computing capabilities, substantial reduction in data storage costs and greater belief of businesses in analytical models has fueled the growth of businesses across the globe and demand of skilled professionals across all levels. However, businesses are demanding high level of performance from their Analytics Service Providers (ASP’s) and are increasingly insisting on translating spend into real tangible, quantifiable outcomes. In the given scheme of things, it is quintessential to measure and improve the performance level of analytics team while simultaneously juggling with the talent crunch of analytics professionals.

As per a recent study a recent survey of 300 IT professionals, conducted by a company called InfoChimps, a mind boggling fifty-five percent of big data analytics projects are abandoned.

And not so surprisingly, the biggest impediment topping the charts was the talent crunch. Almost 80% of the survey participants highlighted that the top two reasons why analytics projects fail are

• The inability of managers to connect the dots around data to come up with relevant actionable insights
• Lack of appropriate business and domain context encircling the data

The Common Analytics “Fingerprint”

Typical analytics skill set is predominantly different from the usual IT ones (more technical or programming-oriented. Primarily there are 3 key roles in analytics: Data Management (includes data assimilation, cleansing, harmonization etc.), Data Modeling or the Data Scientists roles (one who build models) and Data Visualization (the reporting piece). Following skills at an aggregate level are crucial to high-performance of any analytics team but in a nutshell CREATIVITY and CURIOSITY is the most crucial element cutting across all:

• Good with Numbers & Statistics
  • Simple linear regression, basic statistics, hypothesis testing, Z- and T-test analysis can get you so much so that you can take those baby steps in Analytics. But to tame the real BIG beast at other times, you definitely need advanced statistics skills when the data becomes voluminous, unstructured or even when you are headed for predictive analysis
  • Ability to triangulate numbers & doing back of the envelope calculations is imperative and is being commonly used to evaluate potential candidates looking for venturing into the world of Analytics
• Data management capability
  • You shall be headed to nowhere unless your data isn’t clean and enough to perform further analysis,
  • Ability to take calculated, educated risks; especially when it comes to taking assumptions, supported by valid arguments and a strong business sense
• Business/Domain Know-how
  • Deeper understanding of the data and business problem at hand
  • It’s equally important to contextualize outcomes for relevant insights which the business can pursue
• Visualization Capability
  • Represent complex data in a simple and easy-to-understand way
  • Ability to effectively present findings; intuitive to the decision maker especially when the consumer is a business user
• Psychological Skills
  • Being pragmatic, overcoming cognitive dissonance, bias, over-confidence, conflicting thoughts or situations
  • Maintaining extremely high sense of quality, standards, and detail orientation
• Storytelling Ability
  • Ability to connect the dots, from data to insights in a compelling way, understandable by the business user
  • Structured thinking process (especially when the job requires you to deal with unstructured data and complex business situations which may need a well-structured approach)
• Innovation Quotient
  • Can the individual see beyond the ordinary! Cognitive “attitude” and willingness to search for deeper knowledge about everything
  • Ability to productize ideas e.g. packaging a predictive model as a point solution (targeting specific business challenge with a specific approach to deliver tangible business outcome) OR creating reusable assets out of usual business deliverables which could be easily cross-pollinated and applied to other business problems or even industries

In addition, an analytics professional should have at least some of the following capabilities:

• Strong interpersonal skills, effective oral and written communication and ppt skills
• Agility, take a detour based on inferences being reflected in the data
• Passionate about stumbling upon interesting business problems and inclination to solve them
• Proactively seek clarifications and ask appropriate questions based on what’s shared

How to Evaluate High-performing Analytics Teams

Evaluation is primarily based on which track in Analytics an associate is aligned to (Business, Technology, Delivery, Domain/Industry, Modeling/Data Scientist). Due to the inherent nature of how the Analytics industry works or what clients expect out of us, it eventually boils down to quantifiable business impact, either it’s increased top-line or decreased bottom-line. Eventually it boils to Following are the key pillars of evaluation:

Analytics Pillar

• Data Management Capabilities
  • Use of Data treatment techniques
  • Quality of assumptions taken
• Quality of Analytics Deliverables
  • Output Accuracy & Feasibility
  • Visualization ability, intuitiveness
• Analytics SME Quotient
  • Domain Knowledge
  • Analytics Acumen
  • Certifications, Trainings & other Up-skilling/Re-skilling initiatives undergone
• Going the extra mile !
  • Identify, conceptualize & execute new solutions, Analytical concepts, techniques and / or prototype tools  for a market or cross-pollinating ideas
  • Going beyond the call of duty
  • Mentorship, Internal trainings etc.

Business Pillar

• Business Knowhow
  • Domain understanding and knowing how the industry operates
  • Understanding of client’s ecosystem
• Quality of Insights/Recommendations
  • Client-centricity, Business acumen
  • Quantifiable top-line or bottom-line impact, value creation
  • Tangible business outcomes  & how it impacted client’s business
• Curiosity to work on most important business problems, ones which add value to the client

That data also shows the No. 1 reason analytics professionals leave their jobs is because they’re bored.

http://www.allanalytics.com/author.asp?section_id=1411&doc_id=266183

Soft Skills

• Effective Presentation & Business Communication
• Collaboration/Team Player
• Coaching & mentoring
• “Winning @ Workplace” attitude, Self-motivated
• Adaptability
• Decision-making skills
• Negotiation skills
• Leadership traits
• Cultural Fit