Team Data Science

The Fix Is In

photo: JD Hancock

Big data are a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.

So say the opening sentences of the “Big data” article in Wikipedia. The people primarily responsible for conquering those challenges are data scientists. Being a data scientist these days is rather like being a Renaissance person; one must possess knowledge of and competency in a wide variety of subjects related directly and indirectly to the fields of mathematics and science.

Fortunately, data science has a number of sub-specialties to share the load. Understanding–defining–who does what (capturing, curating, storing, searching, sharing, transferring, analyzing, visualizing, processing) and why they do it means companies building data science teams can intelligently choose the areas of specialization that will best serve their goals.

Five Roles You Need on Your Big Data Team

Of course there’s the data scientist, the coveted knight in shining armor who visualizes models and creates (and continuously optimizes) sophisticated algorithms to transform data into something useful. But she could not do her part to fulfill corporate expectations without the support of equally coveted

  • Data hygienists, who deal with the “dirty data” problems inherent in collecting data so the data is clean now and stays clean in future.
  • Data explorers, who burrow into all the data a company collects to determine what, if anything, can be done with it, including how data originally collected for a different reason might be repurposed.
  • Business solution architects, who structure and organize data so it’s properly updated and where it needs to be within the necessary timeframe of every query–a critical feature of today’s data science when queries are ‘answered’ in real-time.
  • Campaign experts, who, with an in-depth understanding of both the technology and marketing, can turn the knowledge derived from data into insight and then into advice.

Assembling a powerful data science team, whether that team is internal or third-party, is necessary to applying big data tools. However, success rests as heavily in the hands of the right corporate culture as it does in the right specialized people. The best solution? Welcome reevaluation, innovation, experimentation, and keep the focus on the end game.

Scarce and growing scarcer

Being able to use the knowledge derived from data, achieving the insights to which data can lead is the centerpiece of a marketer’s requirements in the big data era. But before you can acquire knowledge, you must understand the data itself and how its patterns fit together and suggest other patterns, how to work with it to produce useful, meaningful knowledge. Enter data scientists.

Employment opportunities for data scientists are growing. They will continue to grow, and some institutions are putting educational programs in place to help meet future demand. However, projections suggestion the demand for data scientists will soon exceed their availability. A compelling graphic synthesizes the problem.

Top skill set Requirements to be a Data Scientist

Data scientists aren’t data analysts. While the two roles may start with a grounding in scientific and mathematical skills, a data scientist is far more a “Renaissance individual who really wants to learn and bring change to an organization,” says Anjul Bhambhri of IMB. About a data scientist’s skill set, Mark van Rigmenam writes,

They need to have statistical, mathematical, predictive modelling as well as business strategy skills to build the algorithms necessary to ask the right questions and find the right answers. They also need to be able to communicate their findings, orally and visually. They need to understand how the products are developed and even more important, as big data touches the privacy of consumers, they need to have a set of ethical responsibilities.

Often, related fields of study pair with a breadth of programming, managing, processing and curating skills to shape the qualities of individuals who will guide a business’s effective use of data. Rigmenam suggests an ideal data scientist would have the following skills.

  • Strong written and verbal communication skills;
  • Being able to work in a fast-paced multidisciplinary environment as in a competitive landscape new data keeps flowing in rapidly and the world is constantly changing;
  • Having the ability to query databases and perform statistical analysis;
  • Being able to develop or program databases;
  • Being able to advice senior management in clear language about the implications of their work for the organisation;
  • Having an, at least basic, understanding of how a business and strategy works;
  • Being able to create examples, prototypes, demonstrations to help management better understand the work;
  • Having a good understanding of design and architecture principles;

We would add, while an effective data scientist requires latitude to consider and experiment (work autonomously), she must also be able to work cooperatively. Data scientists are members of teams that aren’t simply made up of senior leaders. There are plenty of other employees who work in the trenches with ideas about situations that require solutions and how solutions would fit into goals of other departments. Failures in cooperation and communication can lead to costly disasters.

Likely, few data scientists possess all the above qualities, so a business should prioritize the ones important to them.

In planning for apply new technologies, businesses must also plan for how they will apportion responsibilities for critical data science needs–through third-party applications or data-science-specific internal departments or perhaps, both. At present, we are gazing at the tip of the big-data, data-scientist iceberg. Demand for big data solutions is increasing. So is the demand for the innovators behind the solutions.

The Conversation: Everything Old is New Again?

1968 Dodge Charger R/T - 2560x1600 Desktop Wallpaper Black BackgroundHow new is big data in the big scheme of things? Will it “replace ideas, paradigms, organizations and ways of thinking about the world”? Or has it evolved from a series of perspectives that have been with us for some time? Is it a development parallel to the invention of the telescope and the microscope, where one can see both big and detailed pictures? What are the trade-offs alongside the benefits? A bigger view of big data.

Sizing Up Big Data, Broadening Beyond the Internet

The Conversation: Customer Relations Creepiness

Evil monkey from the movie about the evil monkey that smiles awkwardlyForget the creepiness of Google knowing so much about you it can recommend a restaurant based on your eating-out patterns (woe to you and the little gem of a restaurant Google is not going to recommend). The real creepiness (maybe) is when customer relations staff can’t get the essentials of customer relations just right.

In the world of handy big data apps and more, we need to ask ourselves how a business introduces “situational awareness” to the ways it conducts interactions with customers.

When Digital Marketing Gets Too Creepy

photo by: scragz

Conversion: Big Data’s Sidekick

Kick me (Explored)You can’t have missed the buzz about how big data tools can take your business to a whole new level. They can, but, by themselves, they are not going to solve all your business challenges. Often, they suggest opportunities you can turn into insight and specific solutions. But, other factors directly influence how successful your big data strategies will be.

One of the most important factors influencing success is integrating conversion rate optimization principles into your business practice. This was important back in the mid-90s–back in 1995, Amazon hit the ground running with conversion principles firmly in place. It’s just as important today.

Do you want to know how satisfying your customer experience is? Look at your conversion rates (the number of visitors who made a purchase / the number of visitors who came to your site during a set period of time). That metric isn’t the only one you need to follow, but your conversion rate is first and foremost a measure of your ability to persuade visitors to take the action you want them to take. It’s a reflection of how effectively you satisfy your visitors and customers. For you to achieve your goals, your visitors and customers must first achieve theirs.

If you want those big data tools to work for you, you need to pay relentless attention to the principles of conversion rate optimization. They are quite simple. Getting them just right is the piece that requires you to roll up your sleeves.

  • Great brands, products and customized buying experiences naturally generate better conversions
  • Follow the hierarchy of optimization (functional, accessible, usable, intuitive, persuasive)
  • Master the conversion trinity (relevance, value, call to action)
  • Understand optimizing your conversion rate applies to your entire site; it’s not limited to a landing page
  • Optimization is not a one-time event or project; it’s an ongoing process

Optimizing conversion rates is not exciting. It’s boring, repetitive, detailed, but necessary work, much like general management. (However, big data testing tools—Monetate is one example—can automate and alleviate much of the drudgery.)

There is a part of the big data/conversion equation many overlook. Data is a valuable commodity no matter how much of it you have. Some businesses are not mentally or structurally ready to shift their efforts into big data territory. Some businesses simply can’t afford it at this point—big data tools are becoming cheaper, but they aren’t yet a Global 5 Million, as opposed to a Fortune 100, solution. And, frankly, big data tools may not be the right choice for all your businesses needs.

As you monitor the big data landscape, keep in mind basic analytics are still a powerful way to understand how to improve customer satisfaction. The granularity of information you can derive from big data creates a much better picture of what is happening in your relationships with customers, but the benefits of conversion rate marketing do not depend on the degree of granularity alone.

Data is nice. Tools to turn data into information that can provide insight is nice, too. Putting however much data you have to good use within a conversion rate marketing framework is essential.

photo by: pasukaru76

Email, Relevance and Big Data

Prettied up and ready to packIn fewer than twenty years, doing business online has developed similarly to the rise of a celebrity discovered in the morass of obscurity and thrown into the limelight. Put the trajectory from Cool Site of the Day (founded 1994) to omni-channel marketing in perspective; that’s pretty meteoric growth in the big scheme of things. The upside is a meteoric rise to fame. The downside is a meteoric rise to fame.

Using the internet as a venue for conducting transactions, many businesses and consumers alike have gone from guarded skeptics to full-blown enthusiasts. But in the middle of the enthusiasm lives an oft-overlooked truth: people are humans. They have preferred ways of interacting with the world, and they have individual needs. For marketers, this is the fly in the ointment.

Back in the old days when many online businesses were trying to find solid ground, email marketing was the rage and high-performing lists were the grail. In short order, ‘personalization,’ with the goal of creating emails that made customers think the company looked upon them as individuals, became the imperative. Still, content was targeted to a segmented though nevertheless large audience, not to one person.

That level of customer attention offered a significant conversion boost over direct-marketing-esque emails. But it hasn’t had the staying power companies hoped for. A recent survey shows respondents favor ‘older-fashioned’ ways of interacting with a company, at least when it comes to learning of new products. Even conventional print catalogues out-perform current darlings like social media, sometimes by two hundred percent.

Intro to Product
But anymore, customers aren’t terribly interested in personalized email. They want customized email: recommendations for products they might like; content that is specific to them. And when they are on the company’s website, customers want tailored content. (Sounds as though they want the Amazon experience in all their online encounters with businesses.)

What is the common denominator in these desires? Customers want relevance. This should really come as no surprise. Customers have always wanted relevant experiences when they make purchasing decisions off- or online. Who among us isn’t a customer? Who wants to give over precious time to stuff that doesn’t matter in the least to us? If we are in control of what we do with that time, we’ll head for the relevant experiences every time.

Conversion rate marketing should be about giving customers what they want so businesses can get what they want. Conversion rate marketing has always been about providing relevance to customers. So, if conversion rates are greatly improved when communications are customized, then customization needs to be a marketing priority.

Send one customer an email that no other customer receives and do this for all your customers? Big data technologies make it possible to do exactly this, even when your customer base is large. But few companies today have the ability to use data at that scale when creating relevant experiences for individuals requires a lot of data, a lot of processing and a level of technology many companies do not have and cannot afford. In reporting results from the above-mentioned survey, Ayaz Nanji writes,

  • Almost half of marketing executives surveyed (45%) indicated that they lack the capacity for analyzing “Big Data.”
  • 50% of marketing executives said they have inadequate budgets for digital marketing/database management.
  • Only 24% of marketers always use data for actionable insight. This limited competency in data analysis is viewed by 45% of executives as a major obstacle to implementing more effective strategies.
  • Only 27% of the marketing executives surveyed said they always integrate customer data from different sources into a centralized customer database.

These are not cheery statistics.

The downside to any online business’s continued (or improved) success finds its expression in an inability, sometimes an unwillingness, to participate in the trajectory of technological developments. Budget constraints cannot be minimized, neither can colleague and leadership resistance. However, no company can afford to overlook one of life’s basic tenets: people want to be treated as individuals not as masses. Delivering what individuals really need is critical. Providing relevance has never been negotiable, and, increasingly, big data technologies allow businesses to take advantage of and relate all the data they collect to offer relevance to each customer.

Today, the money is in the data. It’s time to regroup, even if it takes small, incremental steps such as starting to customize emails. Time to remember what’s important and offer it at whatever scale you can accommodate. Time to consider whether outside providers can do for you what you cannot (currently) do for yourself.

Time to meet the challenges inherent in an industry’s meteoric rise to fame.

photo by: lisaclarke

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