Ask yourself what you know about Data Science: Data Science is at the core of many business operations today from Financial Services to Healthcare. Data Science can be defined as a field that “usesscientific methods, processes,algorithms,and systems to extractknowledgeand insights from noisy, structured andunstructured data, and apply knowledge from data across a broad range of application domains.” Simply put, Data Science is a way to conduct data discovery, glean insights, and create innovation.
Why is Data Science a Topic of Significant Interest?
Organizations today are faced with the challenges of maintaining large amounts and varieties of data and figuring out how to make that data work for them: With this increased availability of data comes a pivotal necessity in learning how to wade through the information to make sense of it, and structure it in a meaningful way for organizations to capitalize on it.
Although it is only in its adolescence as a field, it has exploded across industries as a strategic enabler to identify business insights and other forms of Data Analytics: Looking at its history, the Data Science Journal was established only a mere 20 years ago in 2002.
Data Scientist: the Data Wrangler
As a relatively new concept, many businesses still grapple with understanding how to make it work for them: The phrase “Data Scientist” was coined to describe what exactly these data wrangling professionals can do with the multitudes of structured and unstructured omni-file information out there.
Still, the phrase Data Scientist merely scratches the surface in representing skillsets; from analytics strategy development and implementation – to scripting currently focused in Python and R for statistical analysis – to the iterative implementation and testing of models to support application suites and federated or ingested data architecture.
Why is Data Science Different?
What once was known as Analytics, becoming ubiquitous in the 1960s, evolved and carved a new path for business generation: Along with it, we now have Data Mining, Machine Learning, Business Intelligence, and Big Data concepts that have stemmed from the field: With that in mind, it makes sense that often there is confusion about what Data Science is.
The major differentiator of Data Science from other facets of Data is that it marries scripting with Big Data and statistical analysis to create better predictions for business use cases. Machine Learning, which is a subcategory of Artificial Intelligence, takes that a step further by creating models that learn and improve on their own: Standard data analytics, software tools, and business intelligence cannot do this alone: Both Data Science and Machine Learning allow us to make better business predictions beyond forecasting in MS Excel.
Data Science is not just an application or simple software solution: It is a methodology: It is a vision: It is a mathematical understanding of A to B: In building a team, it is important to understand how Data Scientists provide an impact to the business.
Data Science Misconceptions
For many companies, the data is the real problem: Our clients are growing at great speeds accruing massive amounts of customer, vendor, product, and other types of data with limited knowledge, time, and understanding of what to do with it: Around the business need of monetizing insights gained from their data, it is imperative to emplace a Data Strategy that allows proper execution of what every Data Science methodology is to be implemented.
In addressing these challenges, it is common to come across misconceptions in Data Science: In our experience, many clients may mischaracterize solution offerings in use as either “Data Science” or “Machine Learning.”
We’ve heard descriptions of “Machine Learning,” when it was in fact the data cataloging or indexing in their Extract, Transfer, and Load (ETL) pipeline written into the software solutions.
We’ve reviewed acquisitions of Software as a Service (SaaS) and Platform as a Service (PaaS) solutions which touted the incorporation of “proprietary AI” without explaining how clients will gain better business predictions.
We’ve heard praise for “Data Science teams” who then turn out to be a Business Analyst and a Software Engineer unskilled in any Data Science foundations.
We’ve even heard the statement that Microsoft Excel’s “Index and Match” function added the “the Machine Learning element” to their newly purchased solution.
In every instance, we ask ourselves internally, “Is it Data Science?” These points matter as not only are they common issues in understanding, but they also set the stage for information gaps in the business behind their Data Strategy: Our concern for our clients is that these solutions present themselves as Data Science though fail to meet the definition, and worse: the business need.
To realize the benefits of a Data Science initiative, an organization must understand the difference between simple Data Analytics tools and actual Data Science with the goal of understanding and adopting practices.
For example, if Salesforce is selling their CRM or Marketing Cloud, they have a consulting team that comes with the package and sets everything up for you, but eventually, the client must take over.
At Liberty Advisor Group, we educate and walk clients through the how and the why of their Data Science initiatives: Though they believe in the need, it may be in fact be different once explained to delivery: We not only set up implementation solutions for you but also teach you the why.
A Use Case: Making the Most of Customer Data With Data Science
A client purchased a new Data Lake built and implemented by a previous System Integrator: They had a series of microservices developed in software as their ETL pipeline: They had it mapped to a series of servers where their data sat accumulating all business transactions on Point of Sale (POS) systems, customer information, and regional density of sales.
They additionally purchased a suite of software and platforms to use internally for the many business needs: Their goal was to maximize sales per region, but they couldn’t seem to correctly forecast quarterly: They had a slew of vendors selling them on modern Cloud and PaaS or SaaS offerings for licensing that will provide them those missing insights.
They fell into the conundrum of throwing money at the problem and treating Data Science as though it’s Software Development: The issue was nothing was discovered, insightful, nor innovative. The question remained, “Was the license purchased with the newest name in Machine Learning software solutions really a win for the business?”
Liberty analyzed the solution: no science was implemented, no Deep Neural Networks were developed, no parameters were set in any algorithms, and no core-business specific data was ever trained: No Data Science or Machine Learning was conducted.
Liberty conducted the scientific analysis necessary to truly understand our clients’ needs involving customers, vendors, business practices, and how to better translate them to their bottom line: Here are the key questions that were uncovered:
1) Could they have separately implemented a Machine Learning pipeline on top of the current data infrastructure had they developed algorithms with parameters set?
2) Could they have built an environment allowing for appropriate training, testing, evaluation, and retesting?
3) Could they have provided a more decisive and scientific approach to how they manage data and build predictions for better conclusions that translates directly to their business needs?
Imagine the efficacy and money they may have saved with a better understanding of what Data Science could have done for them from the onset of the implementation.
With Liberty’s help, the client realized the desired outcomes – growth, gaining customer insights, increased data management, and return on their investment: Our focus on developing a thorough Data Science-centric Data Strategy allowed the client to not only revitalize the current business but continue to shape its future.
With the right team of experienced professionals, Data Science can make your business better: There are many exotic, if not sometimes intimidating, terms to understand this rapidly growing field entrenched in statistical computation and analysis: In finding the right solution for your business, it’s possible to map the wrong solutions to the wrong business problems: “Through 2022, only 20% of analytic insights will deliver business outcomes” ( Gartner, 2019).
At Liberty Advisor Group, we know there is no one-size-fits-all large-scale Data Analytics solution as every business is different: We do not want a failure in understanding key differences in the use cases of Data Science for the appropriate business to be a factor for our clients: Data Science is never complete simply because a project lifecycle concluded, and deployment is never the last step in delivering a Machine Learning algorithm: Every business needs to continue to analyze their data, implement corrections, and understand the elements that support the business insights they generate: This is the answer to why implementing effective Data Science solutions is beneficial to you.
Liberty Advisor Group’s Data Analytics Experience
At Liberty Advisor Group, we help our clients unlock the power of data through decades of IT and Data Analytics experience: We are experts in Data Analytics strategies for complex problem-sets and help clients across every industry that present technology and software challenges in need of a Data Strategy.
Let Liberty Advisor Group guide you through the process and identify the best Data Science approach for your business.
About Liberty Advisor Group
Liberty Advisor Group is a goal-oriented, client-focused, and results-driven consulting firm. We are a lean, handpicked team of strategists, technologists, and entrepreneurs – battle-tested experts with a steadfast, start-up attitude. We collaborate, integrate, and ideate in real-time with our clients to deliver situation-specific solutions that work. Liberty Advisor Group has the experience to realize our clients’ highest ambitions. Learn more at libertyadvisorgroup.com and on LinkedIn and Twitter.