In a world driven by data, business analytics and data analytics can work together to maximize efficiencies, uncover actionable insights, and help businesses drive value. Data analytics is an iterative study of data in an organization, focusing on applying statistical and other techniques to uncover insights that may help to promote innovation and the financial performance of a business.
Raw data holds lots of information, and data analytics helps people examine the data, turning it into actionable insights that can help grow your business. For example, data analytics can help businesses maximize a customer’s lifetime value by drawing from historic data. Or it can recommend whether a business should develop a new product line, or if it should prioritize a particular project over others. Data analytics are most suited for improving the precision of lead generation and automated pre-sales processes, because companies leverage a wealth of data sets to pinpoint the right customers at the right time.
Using data analytics, small businesses can enhance customer experiences and deliver superior customer services. Companies can also use data to increase their audiences, which could result in more customers or subscribers, as well as greater revenues. Data analytics can help small businesses to shift the way they market products, interact with customers, and manage finances.
Businesses can dig deep into the data behind their operations and uncover valuable insights about their competitors and their niches. The benefits of data analytics are endless. Data analytics can be implemented across every department, from sales to product development to customer support, thanks to easily accessible tools with intuitive interfaces and deep integrations to a variety of data sources.
All of the above being said, raw data cannot be simply dumped into a business analytics platform to do its job. To make sense of all this data, and to leverage it for greater competitiveness, companies need to apply both data analytics and Data Science.
When companies strengthen their data culture with technological investments, they are supporting data skills and developing the infrastructure that will allow wider-scale decisions made using data, improving behaviors and beliefs throughout an organization. Through a successful strategy, companies must ensure that their data analytics activities are trustworthy and relevant, providing value throughout the entire data journey.
With the right data analytics strategy, business leaders and operators are better equipped to solve data and analytics use cases, and thus create value for their customers and stakeholders with visualizations, reports, or dashboards that help address particular business challenges and answer pressing questions.
From an Idea to a Proposition: Selling Data Analytics to the C-Suite
To initiate and carry forward an IT-related issue in an increasingly budget-conscious and uncertain business world, IT stakeholders need solid support from the business leaders or C-suite executives. A technology idea or solution can sound great, but until the actual benefits to the business are visible through industry data or market-proven vendor solutions, many “proposed technology solutions” remain ideas that never get past the tech skeptics of the business world, who mostly control the finances.
Alan D. Duncan, Distinguished VP Analyst at Gartner, says:
“Vague statements about D&A driving more effective business decision-making won’t be enough. Explicitly develop the value story or thinking, and follow the SMART approach, where business benefits are specific, measurable, achievable, relevant and time-bound.”
Here are some common challenges facing stakeholders interested in selling their “business case” for data analytics to top management (usually C-suite executives):
- Lack of available data validating the power of data analytics: Business functions can begin by relevant data investigation and gathering. Once the relevant data has been gathered, involve seasoned data analysts to analyze the data because CDOs frequently fail to correlate data with business benefits.
- Lack of executive understanding about the role data and analytics play in an organization: Top executives of an organization may not be rightly informed about data and analytics, and about what is needed for success with data for decision-making. They are not equipped to build the vision of the organization, and therefore allow data and analytics to remain siloed within function areas.
- Lack of adequate finances for data analytics: Like many strategic investments, data analytics projects may face challenges competing with other pressing business needs for funds and personnel. Data analytics need executive buy-ins before adequate finances can be allocated for leading a value proposition to an implementable program.
- Lack of one-on-one interactions between data analytics advocates and C-suite: To sway stakeholders from technology skepticism to technology advocacy, data professionals need to sell their vision and strategies to high-level business communities controlling the financial aspects of decision-making.
- Lack of data literacy throughout the organization: Low data literacy will just add to the communication gaps already prevalent between the business systems and IT teams. Organizational teams, no matter which business function they belong to, must first embrace a common language and vocabulary to discuss data-related challenges.
Workable Solutions to Problems Getting Executive Buy-In
Let’s begin with the first problem: lack of available data to sell a business idea or solution. To counter this issue, existing IT departments or data analytics teams need to invest substantial time and effort to collect the “right” data. The data serves as documentary evidence of claims made on behalf of data analytics, and can reap tremendous dividends by supporting a broad range of business decisions. Anchoring data-driven findings directly to high-level strategy leaders may make it easier for executives to understand the value of data analytics.
Problems two and three stated above are intrinsically related because the C-suite may directly control financial decisions or budget allocations. Forrester research states that nearly 50% of data professionals have admitted that the “C-level executives do not fully support the data and analytics strategies in their organizations.” So, before anything else happens, IT departments and teams need to devise strategies to connect with executives, and create an aligned vision of business goals, to show how data analytics help them make better business decisions.
The solution to the fourth and fifth problem stated above – a lack of interactions between data analytics advocates and C-suite, and low data literacy – will be discussed in separate sections that follow.
Face-to-Face Meetings to Reduce Communication Gaps Between IT Teams and the C-Suite
Industry research data has repeatedly demonstrated that face-to-face (F2F) meetings have led to executive buy-ins in important corporate matters. For example, an informal meeting facilitating direct demonstration of data analytics benefits will probably influence the top decision-makers to consider a data analytics solution in the market a lot quicker than fragmented, individual efforts to influence the top IT managers or other stakeholders within an organization.
The F2F meetings provide a grand opportunity to a person with an idea or solution to sell the idea through a combination of oratory, verbal, and visual techniques that are simply not available in a formal presentation or a seminar. Formal presentations rarely lead to actions, whereas an informal gathering of key personnel can trigger conversations about action plans, solutions, vendors, and timelines.
Data teams need to present their findings and insights in such a way that the executives quickly and clearly understand the implications of data analytics for the future of business. One thing to keep in mind is that the C-suite executives are very busy with their own roles, and they do not have the time to sift through heaps of raw data or poorly labeled charts or graphs.
It’s much more effective to walk them through the data:
- State a particular problem or challenge currently faced in a specific business function
- Explain how data analytics has helped mitigate that challenge
- Share supporting data and explain what the data means
- Finally, correlate the data to a “suggested benefit” of data analytics
- Emphasize the Data As a Service (DaaS) idea
If the meeting hosts cannot respond with concise, concrete answers, such meetings will fail to establish a connection with IT (data analytics advocates) and C-suite. The wider the net of stakeholders who see concrete value in improving the organization’s data analytics activities, more likely the top-level leadership will be to buy a proposition.
Initiatives to Address Data Literacy Gaps in the Organization
IT teams can kickstart data literacy initiatives to improve communication and engagement and build data analytics prototypes using real company data to spark discussions among all business users, managers, and top executives. For example, a collaborative kickoff workshop can assign a project manager to get the business and technology teams to start exploring business data together.
Executives need to educate themselves about the value and quality of data within their business and identify ways to best leverage this critical resource. Just as a way of example, adopting a corporate-wide, master data management (MDM) strategy can be a critical step in helping create data consistency, uniformity, and accuracy.
While ideas, emotions, and gut feelings may fluctuate, data analytics is one method that can be used to help business leaders make rational decisions. A sophisticated layer of collaboration is required, and it needs to permeate an organization, eliminating silos of data and data processes.
The ultimate goal of an enterprise-wide, data analytics initiative is to present business information as a single version of the truth, which is more robust, easier to access, and more useful across an entire organization. Cross-functional project teams should focus on setting up and maintaining data analytics practices, managing data across the organization to meet business or regulatory requirements.
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