Data is everywhere. From marketing to product development, leveraging data has become the key to success for several businesses. Still, not all businesses use data in the same way or for the same purpose. For many businesses, it can be difficult to collect, analyze, and use data to build business strategy.
Leveraging data is essential to strategically growing a business. We’ll explore what leveraging data means, what types of data you can use, and how to build a data-based business strategy.
Leveraging Data
Leveraging data involves analyzing data from a variety of sources to measure performance and process, set strategic goals, and guide improvement. This not only involves collecting and analyzing the data but also requires companies to act on the insights from that data.
Ways to Leverage Data
Although “data” can be a buzzword, it’s important to recognize that data is not a one-size-fits-all solution to business problems. Different businesses collect different types of data and therefore have different needs. Data can be used for a variety of purposes, depending on the nature and needs of the business.
Here are some ways that data can be used:
- Identify customer preferences and trends. This could enable the tailoring of products and services to meet market demands.
- Inform marketing strategies. This might include segmenting audiences into different groups based on their purchase or usage data or creating personalized messaging for different customers.
- Understand user behavior. User behavior, including user experience data, could provide insight into how customers interact with a product or service.
Data can only support the business; it cannot change it. So, businesses must start with a solid understanding of their objectives and how to use data to achieve them.
Using Data to Build Business Strategy
Any company can leverage data to build a business strategy. To effectively build a data-driven strategy, it is essential that the company understands its goals and what data it needs to achieve those goals. Furthermore, a successful business strategy should encompass all parts of the business; therefore, it needs to use data that represents all parts of the business.
Let’s use a healthcare system as an example. Healthcare systems need to serve their existing patients and market to new patients. A marketing-focused data strategy might focus on building a customer database and tracking customer interactions. It might also focus on leveraging data insights to inform marketing investments. At the same time, a health-improvement data strategy might focus on codifying patient data to better identify health patterns in a population and proactively allocate resources to serve those populations.
Turning Data into Insights
Effective business strategy begins with accurate, reliable data. To complete analyses and derive insights, it’s also important to understand which audiences will be using the data and acting on the insights. Lastly, integrating analytics seamlessly into daily workflows can ensure maximum adoption. Here are three crucial steps to turning data into insights and strategy for your company
Focus on Data Quality
When analyzing business data, it’s important to start with quality data. Data quality is the measure of how well-suited a data set is to serve its specific purpose. Measures of data quality can include the completeness, uniqueness, and timeliness of the data collection. Quality can also reflect the accuracy, consistency, and validity of the data. Essentially, does this data measure what you want it to measure? Is it representative of your business or target audience? Additionally, could this data collection be replicated in the future?
There are many ways to improve data quality. For instance, if your data is spread across multiple files, it may be better to consolidate it at the source or plan for regular maintenance. Another example would be to ensure that customer information including phone number, email address, and physical address (such as city, state, and ZIP code) are formatted correctly. This ensures the accuracy of your marketing and reduces the likelihood of split records.
Tailor Data to Your Audience
Before creating a platform to provide insights, you should know which users within your organization will use the data platform. This is crucial to ensure that the projected business impact is realistically attainable.
Different users have different priorities when it comes to data. For example, C-level executives may only be interested in the big picture. At the same time, managers may focus more on specific business drivers. Similarly, analysts may prefer to explore data independently, while business users may just want to consume the insights they need.
Maintain Data Workflows
To ensure that your analytics system is widely used, you should consider how your users will integrate it into their daily workflow. This will have the greatest impact on your business intelligence or analytics program.
You can make the adjustment easier for future users by embedding analytics into an existing system. This can eliminate the need for manual data collection and entry, streamline processes, and make it more likely that your users will take advantage of the insights available.
Data Sources for Business Strategy
Leveraging data can be a daunting task for any organization. Data can come from a variety of sources, from customers to competitors and beyond. This data can be analyzed separate from other sources, or integrated with other data sources to create a more nuanced picture of your business’s place within the market. Explore the types of data you can derive from internal and external data sources, as well as benefits and drawbacks to suing that data.
Leveraging Internal Data Sources
- Customer Data. Customer data can take a variety of forms, including customer purchase history, demographics, and feedback data (such as CSAT or NPS scores). Using customer data helps drive personalized marketing and audience segmentation, improves customer experience, and can drive retention. However, this comes with concerns about privacy and data accuracy, as well as obtaining consent for data collection and usage.
- Financial Data. Financial data relates to the company’s finances, including debts and income. This may include income statements, cash flow statements, or financial forecasts. Leveraging financial data can support strategic financial planning, budgeting, and risk management for your company. Some drawbacks to using financial data include data accuracy and limited access to comprehensive data. This could impact the accuracy of financial forecasts and may present obstacles to complying with financial regulations.
- Employee Data. This may include employee performance records, HR surveys, payroll data, or training history data. Companies can leverage employee data to support human resources decision-making, manage and retain talent, and engage employees. There are some disadvantages to collecting and using employee data. Specifically, companies should always protect employee privacy and anonymity, and make sure that they are complying with labor laws.
- Social Media Data. Social media data can be gathered from on-platform content such as comments on your company’s posts, organic posts created by other users, reviews, and more. With social media, you can leverage real-time customer engagement to analyze sentiment, monitor mentions of your brand, and execute targeted advertising. Although it can be difficult to identify relevant conversations and manage the high volume of interactions on social media, it can provide a rich source of data to leverage.
- Web Analytics Data. Web data can include website traffic, conversion rates, and user experience data. Deriving insights from web analytics data can support website optimization, enable companies to segment customer experiences on their website, and reinforce the effectiveness of digital marketing. There are some drawbacks to using web analytics data. It can be difficult to track user interactions between sites, or as users come to the website from different marketing channels.
- IoT (Internet of Things) Data. IoT is when physical objects are equipped with sensors, software, and other technology that connects and exchanges data. IoT data could include sensor data, machine performance metrics, GPS data, or temperature readings. This enables real-time monitoring of products, predictive maintenance, and process optimization. Although IoT enables rich business data, managing such large volumes of data, storing, and processing that data, and ensuring data security and privacy can be a challenge.
Leveraging External Data Sources
- Market Data. Market data can include surveys, focus groups, industry reports, and competitor research data. Using market data can inform strategic decision-making and fuel competitive intelligence. It can also help your company proactively identify market trends. One drawback to using market research data is that it is costly and time-consuming to collect, which may make it difficult to stay up to date on market trends. Additionally, possible bias from the sources and methodologies may make it difficult to accurately interpret research findings.
- Competitor Data. Like market data, competitor data is essential to any business strategy. Companies can leverage competitor data such as pricing information, product features, market share reports, and customer reviews to benchmark their own performance, conduct competitive analysis, and identify market opportunities based on unaddressed market problems.
- Supply Chain Data. Supply chain data may include shipping records, inventory levels, supplier performance metrics, and demand forecasts. Leveraging supply chain data can improve efficiency, drive cost reductions, and enhance demand forecasting. On the other hand, supply chain data collection from supplies may be inconsistent or delayed. Additionally, it may be difficult to integrate complex supply chain data from a variety of sources.
- Government and Public Data. Public data can include economic indicators such as GDP or employment rates, government census data, and regulatory filings. Leveraging public data can inform market entry or exit decisions, regulatory compliance, and economic trend analysis. Some drawbacks to using government and public data include the risk of outdated or incomplete data, as well as challenges to validating the accuracy of the data or difficulties of customizing the data.
Challenges to Leveraging Data
Companies are gathering more data than ever before. Companies constantly look for ways to turn data into actionable insights to make better decisions. For instance, dating apps analyze user profiles and behavior data to determine matches and place the ads you can see when you swipe.
But as Natasha Joslin, co-founder of data agency Mojo, shares in our Data Chats podcast, bridging the gap between data and business teams remains a significant challenge for any company.
There are several challenges to leveraging data for business strategy. First, there are logistical challenges to managing high volumes of data. Ensuring data quality is essential to prevent inaccurate analyses and insights. Additionally, privacy, security, and regulatory concerns can be difficult for companies to navigate.
From a data engineering standpoint, integrating data from different sources is a complex and time-consuming task. Siloed data and organizational resistance to using data can hinder the leveraging of data. Finally, for any organization to continually leverage data, it’s important to invest in data infrastructure, analytics tools, and skilled team members.
Ultimately, leveraging data from different sources can be difficult, but it enables companies to stay competitive in a changing market. Data-driven insights enable businesses to identify market opportunities and best serve customers.
