Leverage Data For Business Growth with Data Experts
Businesses can no longer remain competitive by looking at single or siloed data sources. Many organizations are actively mining their various data sources for deeper insights to remain competitive. Enlisting the right experts will ensure data delivers timely and actionable insights.
Businesses today view data as one of their most important assets, embracing data and analytics technology as a platform on which to build their competitive advantages. An Accenture study found 79% of enterprise executives believe businesses who do not embrace data will lose their competitive advantage and “risk extinction.” Indeed, stakeholders are adapting to this mindset shift and recognize that data holds immense potential for creating business value.
To derive the most success possible from a data project, stakeholders must have:
- A deep understanding of big data’s scope and sources of value.
- Focus on practical applications and business outcomes.
- Commitment to budget and talent to achieve results.
- An appreciation of the importance and disruptive power of big data.
By recognizing data as a valuable asset, stakeholders can help unlock its potential for creating value in their organization. In this blog, we look at when data is an asset and how to leverage it to drive business value.
When data is an asset
Data becomes an asset when transformed into information that provides valuable information and facilitates actionable insights. Data is only as useful as its reusability or transferability as well as the ability to replicate or combine data with other data to produce new data. Data must be as free of defects as possible to provide value to a business.
In addition, data must be formatted correctly, be timely and relevant to the issue at hand to provide truly actionable insights. Data is best leveraged by a data professional skilled in uncovering patterns that provide insights. Engineering, data/business analysis, and data science are all necessary components to fully leverage data.
Leveraging data for actionable insights
Data can be leveraged in many ways and requires experts to help navigate it successfully. When successfully leveraged, data is shown to lead to 126% increase in profits, increase sales by 131%, and deliver 132% increase on ROI. But how do you get there?
Leveraging data is a journey and as the journey progresses, your organization will achieve greater maturity. Data maturity, as it is called, can be rated on a continuum ranging from “digital naïve” to “digital elite.” The continuum of data refers to how data is leveraged (or not) and informs business decisions. Businesses that are digitally naive most likely have their data sitting in silos either barely informing business decisions or not informing any decisions at all. Whereas businesses approaching digital elite status are utilizing their data to inform business decisions, improve strategies, and drive value-creating results for the business.
To achieve digital elite status, your business should embrace data science experts, whether in-house or through a third-party, to achieve business goals. Experts can help set up dashboards to watch and analyze real-time data to achieve the greatest rates of data optimization possible.
Data science as a method to leverage data
To leverage data, data science is the first avenue businesses explore. Data science is a process of extracting knowledge and insights from data in various forms, such as structured, semi-structured, or unstructured. It involves using scientific methods, processes, algorithms, and systems to analyze and extract useful information. Data science includes data cleaning and preparation, exploration of data, feature engineering, predictive analytics, machine learning, artificial intelligence (AI), deep learning, natural language processing (NLP), and visualization.
The ultimate goal of data science is to transform raw data into actionable insights used for decision-making. According to NewVantage Venture Partners, data delivers the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%). To achieve these results, data is being used for the following:
- Exploring and analyzing data using statistical techniques, machine learning algorithms, and predictive analytics.
- Developing data-driven solutions to complex business problems.
- Creating visualizations and reports to present complex data in an easy-to-understand format.
- Working with software engineers to build models and tools based on the results of data analysis.
- Collaborating with stakeholders across multiple departments to understand their data needs.
- Staying up to date on industry developments and best practices in data science.
- Recommending methods for collecting, organizing, and cleaning large datasets.
- Developing machine learning pipelines and creating models used to make predictions or generate insights from structured or unstructured data.
- Identifying areas for improvement in existing processes or systems related to data collection, storage, manipulation, analysis, or presentation.
It is common to immediately think of data scientists when considering anything related to data. Yet, data scientists don't perform all these tasks on their own. Other data professionals, like data engineers, data analysts, and others are also involved in this process. Together, these data professionals serve as a bridge between raw data, business intelligence (BI), reporting, and predicting based on data.
Data experts are the bridge
With big data talent in short supply, successful users source skills wherever they can find them, often paying a premium for data experts such as data scientists, analysts, and engineers. To overcome the high cost of entry Accenture found enterprises successfully leveraging data are leaning heavily on external, experienced resources.
Dimitris Bertsimas, the associate dean of business analytics at MIT Sloan, states, “The two protagonists in this process are data and decisions, He continues, “Analytics leaders may understand the basics of the modeling, but it is their skillful handling of the data and the decisions that gives them an edge.”
An example of a data science workstream would be obtaining and preparing the data needed for analysis. This would involve collecting and organizing data from databases, surveys, and other heterogeneous records. The data scientist would then analyze that data to draw meaningful conclusions. Finally, the data scientist would present the results of their analysis through visualizations or reports
In our second blog, we will explore more in depth the different roles and responsibilities that make up data science. For now, it is important to understand that data can be difficult to successfully leverage if your business does not enlist knowledgeable experts.
Do data science with the experts
Many organizations are at the start of their data journeys. But as Piyanka Jain, president and CEO of Aryng, states, becoming data driven is a journey with no destination. The process is ever evolving and your business must adapt to what it learns from the data. It’s a constant work in progress requiring knowledgeable experts - experts 95% of businesses are finding from third parties (ie., vendors (34%), contractors (45%), and consultants (57%).
Partnering with experts can expedite the journey while your organization develops internal maturity. Entrust ChaiOne with your data project. Our experts can help drive value for your business by helping you master your data. To learn more, chat with us to see how we can partner.