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