data science and business analytics

With a growing array of sensors, applications, and business models, data is flowing throughout our world. However, it is far less common for companies to use data science and business analytics to transform what they do and how they do it. Making the transition to a data-driven organization is not easy. Data-driven culture begins not just with a vision but also with a foundation of the right data assets, data governance, and talent. The solution requires breaking down data silos, adopting new working methods, and even developing new mindsets. A relentless focus on business outcomes is required.

Let’s start with a question: where do data science and business consulting stand concerning each other?  

Data Analytics and Business Consulting – What’s the Difference

Data Analysts and Business Analysts differ mainly in how they manipulate data. These two roles have quite a few similarities and depending on the company’s size, these roles are often interchangeable. However, each analyst role has its specific data-related functions. Let’s delve into these.

Business analytics helps companies make immediate decisions based on their data

The goal of business analysts is to solve well-defined business problems by analyzing company data, such as costs and profits, to understand past performance and predict future performance. Their findings provide useful insights for making practical improvements to a company. A company’s management relies heavily on business analysts to make daily decisions.

An analyst may come from a variety of backgrounds, such as management, business, finance, information technology, or computer science. In business analytics master’s programs, people from different backgrounds can learn statistical analysis, predictive analytics, data visualization, and effective communication to provide business leaders with clear, actionable recommendations.

 Business analytics does not require extensive coding or math skills despite its name.

Business growth and development are improved through data science

Data scientists use big data and algorithms combined with statistical modeling and computer science to answer broad questions. Patterns are identified by examining variables such as geography, purchase behavior, and seasons. The insights gleaned from them allow companies to plan business strategies for the future.

The skills of data scientists are more specialized than those of business analysts. Data scientists develop algorithms and conduct statistical analyses. They increasingly use artificial intelligence and machine learning. 

Data Science vs Data Analytics – What Can They Do for Your Business? 

Adopting a comprehensive data analytics approach can be like navigating uncharted territory. Where do you begin? A small sailboat could be set sail aimlessly, and you could hope to get to your destination, or you could hire a guide to guide you on your journey.

Which one will you choose? Would you rather go sailing alone or with a guide and his arsenal of wisdom and tools? The truth is that most businesses do need a companion to guide them through this digital journey. Data analysis is tricky, but the goal is to draw business-relevant insights from datasets.

Data analytics has evolved from a simple analysis of the past using Excel to a complex analysis that uses algorithms and machine learning. This analysis is designed to discern trends and predict the future. Companies can now take data one step further with data analytics consulting, which provides suggestions based on patterns found.

Data science and business intelligence analytics are also helping many companies to analyze first-party data (data they already own) and third-party data (data supplied by external sources) to monitor and predict consumer behavior trends more accurately. Organizations that capitalize on internal and external data can outperform their competitors by double digits

 with timely signals and comprehensive insights.

Companies’ strategies to leverage data analytics are not just about investing in the latest ‘shiny object.’  Instead, they are equipping themselves with the tools, the right datasets, and the required expertise to identify insights that will drive growth and lead to a competitive advantage.

The Need for Data Science Consulting  

Data science consulting expedites approaches if you are bound under the following considerations. 

  • Your use case cannot be met by an existing off-the-shelf solution: If companies have specific needs and existing products do not meet their expectations, consulting companies can help companies build customized products to avoid or minimize off-the-shelf solutions risks such as costly customizations.
  • Budgets alone cannot pay for an in-house team: Data scientists, business analysts, data analysts, data architects, data engineers, data architects, etc., are among the roles that make up a data science team. Building such a team will require an expensive proposition.
  • You don’t need proprietary data for data science projects: if your case and data are not unique, your consultant has probably worked with similar data in the past. Leveraging their experience can help you achieve better results more quickly.
  • The data set contains no sensitive information: Companies must exercise caution before sharing data due to data privacy regulations. Data masking and synthetic data generation help companies make their data ready for sharing.
  • If you need help identifying your company’s project’s strategic data science elements, consulting firms are a popular choice. Typically, most companies are specialized in a market and lack knowledge of project strategy and implementation. Consulting firms identify how data science and business analytics can be applied to business processes.

Challenges Companies Face when Adopting a Data Analytics Approach

Starting out on the right foot

Almost every company has access to some level of customer, sales, or supply information. How can you make the most of that data, and which third-party datasets may help you achieve your goals? Often that’s where companies get stuck. What is the best way to identify the right data sources, how can you ensure the security and quality of the data, what are the top data priorities, and who is responsible for keeping these priorities on track? Many important decisions must be made early on to lay the groundwork for a successful digital transformation.

1. Achieving business outcomes through data and analytics

While some companies are making strides in data analytics, many still consider it a tech-oriented project. Technology is important to implement data analytics effectively but is not the starting point. To reach your business goals, identify the business outcomes you hope to achieve first, then determine how you will gather, extract, and analyze the data necessary to accomplish them. Gartner’s survey found that when data and analytics teams are involved in developing their organization’s overall strategy, there is a consistent increase in business value by 2.6x.

2. An overabundance of data with few insights

In most cases, adding more data leads to more data quality challenges. Approximately 45 percent of the time that data scientists spend is spent preparing and integrating data for analysis, according to the State of Data Science 2020 report from Anaconda. In-house data analysts spend most of their time wrangling data instead of analyzing it.

Is data quality a significant problem? Data is collected from a number of sources, including ERP systems, CRM systems, sales systems, HR systems, social listening commentary, customer surveys, and third-party data. The main cause of discrepancies is pulling data from multiple disconnected sources. In addition, any digital transformation that relies on poor-quality data will be more difficult to execute.

3. Data governance lacks a clear strategy

Data governance strategies enable companies to store, cleanse, and manage their data assets effectively. However, many companies stop there. The fact of the matter is that datasets evolve with time, but so do the target audiences, the tools you use, the regulatory requirements you have to meet, the available resources, and even the business objectives you’re trying to achieve. The right data governance strategy should help you manage the change in your organization’s people, processes, and technology.

4. Deficiency of resources

Gathering the right data is the first step, but how do you use it to drive business results? It takes data scientists who understand your business objectives, knows how to identify the right models, and use artificial intelligence effectively to assist you in building optimal predictive and prescriptive models. Learning business knowledge relevant to the problem a data scientist is trying to solve is of the utmost importance to them. Due to the high demand for business knowledge and advanced skills in analytics, it may be not easy to find professionals with both business knowledge and analytic skills. This often results in creating AI or ML models that don’t provide the answers you need to solve your business problem.

5. Access to useful data for those who need it

How can you empower your employees to make better decisions once they have the right data and tools? Often referred to as ‘data democratization,’ proponents of this initiative believe that broad distribution of information can empower decision-making. Nevertheless, many companies have difficulty integrating and connecting people, processes, tools, and information required to support and maintain this type of digital transformation.

Thinking Ahead

Businesses often turn to consultants for assistance during critical transformation/turnaround periods in today’s competitive and challenging environment. Knowledge-driven analytics can be a consultant’s best friend in generating strategic impact and adding value. Consultants can assist client organizations by providing critical and timely insights about their business and operational performance by choosing the right tool.

Ascend Analytics specializes in data and AI for business intelligence across industries. Making them uniquely qualified to help your business, whether to enhance customer experience, reduce costs, or shape products and services.

Data analytics and business analytics can assist consultants by identifying process transformation opportunities by obtaining an accurate understanding of the business’s environment, identifying trends in the business’ data, and making predictions based on these trends. Accelerate and optimize your business by making the right choice with Ascend!

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