Business leaders increasingly rely on insights that can be gleaned from the flow of information that comes from data streams that are increasing daily in variety, volume, and velocity. These insights come through the efforts of marketing experts with the highly in-demand skill of being able to leverage technology and software to craft marketing strategies that drive the business.
As data streams evolve, so too must the methods being used to analyze and understand that data. Depending on the business use case, your marketing team might find itself leveraging data science methods or data analytics tools, which are very different processes. Let’s take a closer look at what each of these disciplines entails and how they are unique, as well as where they overlap.
What is data science?
Data science is the use of multiple disciplines (mathematics, statistics, artificial intelligence, and engineering) to create data models from which meaningful insights can be extracted or extrapolated. It is a process of predictive modeling, where the goal is to combine data streams into a meaningful narrative leadership can use for forecasting trends and making informed decisions.
How can data science benefit your business?
Regardless of the size of your organization, your business can greatly benefit from leveraging data science in your strategic planning process, as the data models built by data scientists can be used to predict and anticipate business conditions.
To get the most out of data science practices, business leaders need to clearly define the problems they are facing. For example, predictive models can help predict the impact of pricing or marketing changes on customer behavior.
With clearly defined goals, data scientists can create models capable of generating data relevant to the business’s evolving needs over time. Through these tools, leadership can gather the insight necessary to analyze economic trends and establish how budgets should be allocated towards specific strategies in marketing and sales.
When should you use data science?
Data science is in itself a broad discipline that encompasses several fields focused on the mining and leveraging of massive data sets, including the field of data analytics. In a broad sense, because data science methods are focused on prediction of future outcomes, data science is best leveraged when your business needs to answer new questions and uncover insights from data streams in order to craft ongoing strategy that takes into account trends in customer demand and behaviors.
In many industries, leaders and decision makers benefit most from data science models, which allow them to extrapolate from current conditions and trends to anticipate customer needs and demand across the marketplace. Some of the industries that benefit most from the predictive nature of data science include:
- Retail-focused businesses
- Health care
- Logistics and transportation
- Finance
- Managed services
- Entertainment and media
What is data analytics?
Data analytics leverages technology and software tools to analyze massive amounts of historical data. The resulting analysis is used by leaders and business owners to guide the decision-making process and help to shape strategy, improve business processes, and drive business growth.
How can data analytics benefit your business?
There are a number of benefits to your business in leveraging historical data through data analytics methodologies.
- Data gathered across different channels allows you to personalize the customer experience to improve product and service recommendations.
- Data analytics tools help to assess business performance and determine where efficiencies can be introduced to streamline operations and business processes.
- Business analytics are essential in establishing risk mitigation strategies. For example, historical data regarding theft across a chain retailer’s locations can help leadership understand which locations are most at risk and further data can help determine what measures will prevent, offset, or alleviate the losses.
- In order to properly manage product inventory or allocate staff to execute business processes, decision makers can leverage historical data to predict sales trends and customer purchasing activity.
When should you use data analytics?
In an economy where it’s almost impossible to achieve a true competitive advantage through the products or services you offer, your ability to dominate your niche and drive incremental sales is dependent upon your company’s ability to increase efficiencies across business processes. In order to accomplish this, leadership needs to be able to make informed decisions.
While business owners and decision makers leverage data science in broad strokes to anticipate future outcomes based on current trends, data analytics strategies are more often leveraged at a departmental and granular level to help HR teams, sales departments, marketing teams, and other stakeholders improve business practices and support the brand’s business goals.
Key differences between data science and data analytics
While both processes work with data to gain insights, there is a difference in the scope that data scientists are concerned with versus that of data analytics specialists.
Data scientists are acting proactively at scale, assessing current data and trending factors to predict the future state that they might be operating under. The models built to accomplish this are broad and designed to find opportunities and form the foundation for business strategy. Data scientists are concerned with establishing what the right questions are and then use algorithms and AI-driven machine learning tools to leverage data models into solutions.
Data analytics specialists operate at a much more granular level, using the same data streams to collect historical data and using the proper tools to solve existing, identified problems and find trends. Analytics specialists are focused more on organizing and curating data, using the database to receive queries from leadership and provide actionable insights and answers.
Different educational requirements and career paths for data professionals
From an experience perspective, the two fields have different educational requirements and scopes of knowledge.
Data analysts are often required to have a bachelor’s degree from fields such as mathematics, statistics and accounting, or computer science. Data scientists on the other hand, require a more advanced master’s or doctoral degree, with a specialization in data science, information technology, advanced mathematics, or statistics.
While practitioners in both cases should have a familiarity with the Python programming language and data visualization tools and techniques are requisite, they are leveraging these tools in different ways.
Mistakes to avoid when using data science and data analytics
As with any business process, there are pitfalls your team should avoid in order to maximize the impact your data analytics and data science efforts have on your bottom line. Many of the mistakes that businesses make when dealing with data revolve around how the data is viewed and utilized.
Your business must be central to the data analysis process
Remember that the goal of data science and analytics is to support the success of your business by identifying and solving problems and opportunities. Your team should be creating plans to address the questions that have been identified, with the focus squarely on improving business processes and providing actionable insight and guidance.
Understand how to manipulate and assess data
When working with data, your team must remember to consider any flaws resulting from the source of data. There might be inherent bias in the large datasets that leads to false conclusions. Further, results might lead your team to assume that correlation means causation, a common error made by those unfamiliar with working with complex datasets.
Use the right tools for the job
When building out your analytics strategy, it’s important that you’re using the right software tools and platforms to support your needs. When leveraging these tools, it’s also important that you are using them to create unique and customized analytics models for each business problem so that the results are accurate. While it might seem faster and cost effective to reuse existing models, they may not be the right fit for the questions that need to be answered.
One of the most powerful tools businesses need to be able to navigate is the Google Analytics platform. Used to track and report activity on your website and mobile applications, it’s one of the most integral data tools for modern businesses to gather actionable data.
Practitioners should also have the ability to navigate several key programming languages and data visualization tools. For example, they should have an understanding of Python for data visualization and SQL for database management in order to keep information organized. Standard tools also include the ability to use Excel and business intelligence tools like PowerBI and Tableau to create dashboards and other interfaces that can be provided to leadership.
How can a big data agency help your business grow?
While there are plethora of analytics tools available on the market, it takes an expert data scientist or data analyst to convert your data streams into actionable insights stakeholders can use to grow the business.
In the coming year, Google Analytics will be making significant changes to the types of data that will be tracked and what data companies will be able to access from users. To prepare for this shift, your business must have the groundwork in place to effectively pivot. Big data agencies can help develop the necessary data models your business needs to establish the necessary competitive advantage to keep ahead in your industry and increase profitability.