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.

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

Imagine a Lamborghini or Ferrari or any car of your choice for that matter, with a fantastic engine parked in the garage, you’d love to get your hands on the wheel, wouldn’t you? After all, it makes no sense to invest so heavily in such a mean machine and yet leave it hidden under a wrap, right? Think of the Data sitting inside the organizations as the great, potential “engine”; an invaluable asset which remains elusive most of the times, well, pretty much in storage, as most CXO’s agree that they’re not doing nearly enough to maximize the use of effective analytics to unleash the potential of dark data they are sitting on. The future belongs to those organizations that effectively employ analytics to understand their markets, customers and operations. Forward-thinking organizations recognize that data is becoming the new source of competitive advantage, and organizations are re-thinking value creation and investing in new analytics infrastructure. In fact, data analytics is routinely cited by CXO’s as among their top one or two priorities year after year. Companies are already making use of data to advance a variety of business goals and to help consumers. Few of the leading organizations who are pioneering in this space of harnessing data for business value (like Facebook, Google, LinkedIn, and Amazon have) shown the world what is possible when data is used to its truest potential in cutting-edge ways, and the idea that enterprises must recommit themselves to become data-driven is now a widely held notion. While many companies have excelled in the use of data analytics and predictive modeling, data-driven decision-making is no slam-dunk. Several companies are struggling to make data-driven decision-making part of their DNA.

Across industries, “Big Data” and Analytics are helping businesses to become smarter, more productive, and better at making predictions. Organizations today are collecting increasing amounts of disparate data. In fact, they are collecting more data than they can manage or analyze; which means most of the data being collected is underutilized. Yet, organizations understand and know that data and data analysis can provide an important strategic competitive advantage. Businesses today are under extreme pressure and face significant challenges to reduce overall costs, improve outcomes, adapt to new technologies, comply with strict regulatory restrictions and face the ever increasing power of the consumer. Organizations agree that building analytics competency can and will drive improved delivery outcomes, quality and cost leveraging the “power” of data. Best in class organizations are adopting analytics to drive decision making, improve outcomes, increase member loyalty/ retention, reduce unnecessary costs, and increase accountability.

Organizations that know where they stand on the analytics maturity continuum are better prepared to turn challenges into opportunities. By performing their current state assessment and building an enterprise value roadmap for analytics adoption, organizations can define the “best way forward” to completely engage a data-driven culture. Tapping this potential for your organization begins with shaping a plan. You have to set a strategy; draw a detailed road map for investing in assets such as technology, tools, and data sets; and tackle the intrinsic challenges of securing leadership buy-in, reinventing processes, and changing organizational behavior. Analytics is not just about generating insights, but getting those insights to the right people. To sustain the long-term success of data-driven innovation, it is necessary to continually revise one’s analytical approach in order to generate insights that lead to new innovation and competitive advantage.

The first stepping stone in the direction or crafting a robust data strategy starts with doing a comprehensive Analytics Maturity Assessment exercise. Inherent question which crosses our minds is, “Why Analytics Maturity Assessment”?

Need for Analytics Maturity Assessment

• The most critical aspect to any organization is to leverage true benefit of data, decipher where they are today, where they’ve been in the past, the progression curve and a direction where they intend to go in the future based on data/information available at their disposal
• By leveraging maturity assessment framework, organizations can measure the current maturity of the data (how good is it to perform analysis) and the overall analytics program in an objective way across various dimensions that are key to deriving accelerated value from data
• Uncover how their data efforts stand in comparison to those of their peers in order to ensure best-in-class insight and support, and ensure we are in tune with the contemporary market trends
• The assessment shall also render guidance to companies at the cusp of starting their data journey, by helping them understand industry best practices used by companies across geos, of different sizes & even from industries that are more mature in their deployments
• After performing the benchmark study, organizations will be able to quantify the maturity of their deployment in an objective way, understand the progress, and identify what it takes to graduate to the next level of maturity

Key Challenges impeding Analytics Proliferation

Organizations want to leverage data analytics but face challenges while trying to formulate a strategy around it due to:

• Lack of Vision
  • Business leadership doesn’t have a defined corporate strategy to drive data driven culture
  • No vision on how to embed analytics into the decision making process
• Disparate Data Sources
  • Data stored in silos across departments
  • Many different types of data sources
  • Large amount of data generated
• Talent Crunch
  • Lack of people with Domain knowledge as well as business analytics expertise
  • Lack of people with knowledge of varied data types and tools to integrate, process and develop insights
• Resource Availability
  • Lack of resources to quickly turn around on-demand analytics
  • Low bandwidth with IT resources to provide near real time information

Maturity Assessment Guiding Principles

In order to ensure your Analytics Maturity Assessment exercise in worth the time & effort, a few guiding principles would come in handy.

• Data aggregation across multiple data sources: Analytics needs to gather the information from multiple sources across business/ functional areas
• Blending existing and new data: Analytics should be capable to use the existing data that is available inside the organization and utilize with the new data outside the organization (social, market research, surveys, competition etc)
• Business user friendly: Analytics should be understandable to all relevant stakeholders intended to be consuming the insights
• Predictive Analytics: Analytics must provide the anticipative/predictive model and should also support “what-if” analysis for different scenarios
• Scalability and Flexibility: Analytics should be able to get customized but at the same time should be scalable and extensible for future needs
• Real-time Analytics Tools/Services: Analytics should be using tools to quickly process the data and translate that into actionable insight

Key Levers impacting Analytics Usage & Adoption

For any organization’s expectations and aspirations, and the current state of analytics takeoff primarily banks on the following five key levers. Key stakeholders who shall be impacted (including CXO’s & Senior Managers) need to be included as part of the assessment workshop where appropriate brainstorming needs to happen on existing challenges being face by business, current maturity of Analytics usage across departments/functions, thorough deep-dive into use cases where analytics consumers share their experiences of how they see analytics as a key ingredient to value creation in LOB’s or departments under their ambit.

• People
  • Identifying key stakeholders
  • Carving out roles/responsibilities
  • Talent needs
  • Change management
  • Training requirements
  • Analytics skills & competencies

• Data
  • Data sources & management
  • Data integration & accessibility
  • Data Infrastructure
  • Aligning data sources to use cases
  • Tools/tech/platforms requirement
  • Data maturity

• Vision
  • Analytics vision and goals
  • Assessment of key BU’s vision
  • Analysing business drivers & needs
  • Executive Sponsorship
  • Business readiness
  • Top down buy-in for analytics uptake
  • Accountability and ownership

• Process
  • Streamline existing analytics, reporting and operational processes
  • Identification of modelling approaches required
  • Benchmarking to best practices
  • Mapping information needs
  • Monitoring and refresh

• Ongoing Improvement
  • Governance, Risk and compliance
  • Improvement Process & Analysis Methodology
  • Data and systems governance
  • Documenting and reporting distribution needs
  • Identification of future investment areas

In the next edition to this data strategy blog, I shall be touching upon the key stages an organization goes through as it matures along the analytics adoption curve, core design principles to execute a successful data monetization strategy, and key data and analytics transformation dimensions to choose the ideal business model(s). Stay tuned !