Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the gold mine of data and help them drive swift business decisions efficiently.

People have tried to define data science for over a decade.  Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, statistics and more. It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data. Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs. Data analytics and machine learning are two of the many tools and processes that data science uses.

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.

Machine learning can be defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning software is comprised of statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data.

Data visualization is the graphical representation of the data and information extracted from data mining using visual elements like graphs, charts, and maps. Data visualization tools and techniques help to ingest and process information and make decisions.  Business intelligence (BI) uses the data from the business operations to transform raw data into meaningful information. It's used for data analytics, data mining and big data to operate the business.  Ideally, they go hand in hand.

Data visualization helps decision-makers interact with data to understand patterns, trends, and insights by transforming data in context that drives cognitive understanding.  Data visualization generates visuals like tables, graphs, charts, images, patterns, animations and more.

Business Intelligence tools and applications are used to analyze data from business operations and transform raw data into meaningful, useful and actionable intelligence/information. Business Intelligence generally relies on historical data to help frame context of present circumstances.  Business Intelligence can then fuel visualization, reporting, and analytics functions.

Data Visualization is used to represent data and simplify decision-making using the outputs from raw data and BI.  Business intelligence is required to refine and contextualize historical data and present ltoday's data in meaningful context.

In a short few years, data warehouses have become an indispensable and integral part of modern business. To ensure that warehouses remain effective and serve their primary design objectives, it’s equally important to regulate and manage the content properly.  Standard rules and procedures are required to ensure a "single source of truth" across the enterprise. The collective of these rules and procedures can be termed as “data warehouse governance”. Since each data warehouse is different, governance policies need to be formulated based on business needs, keeping in mind the key operating elements of sponsorship, organization, and process. 

No governance program can be implemented without the sponsorship of senior leadership. From a hierarchy perspective the senior management provide financial support, enforces compliance, and provides resources for data initiatives. 

The next important component is to establish an overwatch or governance function comprised of tech-savvy leaders from IT and business units.  The purpose of having business people in the governance body is to ensure that the overall data warehouse architecture is aligned with business goals, operational, security and privacy priorities.

With sponsorship and organization in place, the last facet of data warehouse governance is ensuring that fundamental processes, such as fund allocation, program management / acceptance and measurement, are established imprinted on the organization. 

As Data Science and AI become essential to business growth and success, so do the challenges of building a team capable of delivering on the promise.  The need for flawless execution and high-value outputs is paramount.  But as the dust settles, common failures tarnish the high-value potential of the discipline.

As consultants who have worked in a broad range of industries and who have built successful organizations, we’ve had the chance to learn the ropes firsthand.  Many people tend think of data science as a new field, and expect it to have growing pains as it becomes mainstream, but we forget where this field came from - it's simply a newer name for many disparate, related fields -- re-assembled and rebranded.

Are you seeking to build a new function or expand rapidly? The most important, but often overlooked, ingredient is practical business experience. Without the context to understand "what a successful data science/BI organization looks like", it's hard to build one successfully.  The team at Abacus Analytix has a long standing and highly diverse background that allows us to customize and tailor an organizational design that will succeed in your environment - and we can help jump start you with our own skills to help ensure your team hits the ground running or we can also flexible outsourced support services.

Since data is the most essential ingredient of your data science strategy, the first consideration is to build out skills and technology in the data engineering discipline. You need a team comfortable working with big data and cloud technologies, and who know how to build data pipelines, design databases and to pull useful, flawless data from various sources. 

After you’ve built out your data infrastructure, you need people who can take the data, clean it, analyze it, run experiments on it and communicate the results.  You'll need a team and tools to create statistical inference or predictive models, run experiments, plot results, create reports and provide decision insights to stakeholders. 

The next critical function is to enable machine learning for your systems and processes. This isn't about building machine learning algorithms - it's about enabling data-focused developers familiar with various data science libraries and who know how to write production quality code based on the models developed by the analysts - aimed at "industrial deployment" of critical decision-making engines.

The main takeways are — if we focus on strategy, hire or source the right people at the right time, leverage knowledge gathered from previous incarnations and develop a process that works best for your needs and goals, there is no reason you can't build an effective data-driven organization - leveraging our decades of relevant experience.