As a strong strategy for data and analytics becomes a mission-critical component for business growth, small to midsize business data and analytics (D&A) leaders need to scale their approach effectively. Data analytics programs drive integral business movement as they allow leadership to make evidence-based decisions, prioritize resources into the highest-performing initiatives, increase productivity, run more impactful marketing campaigns, and more.
However, small business D&A leaders also deal with large amounts of data that are difficult to aggregate and draw insights from. Also, teams might be potentially solicited, making data access and transparency difficult and reducing its effectiveness. In this article, we’ll explain how effective data and analytics programs deliver value and how to get the most impact in your unique approach.
What are data and analytics for small businesses?
Data and analytics use data sets to identify trends and patterns, informing business decision-making. Using these insights, leaders can increase sales, improve productivity, optimize resources and budgets, and scale their business.
While data and analytics are a huge topic of conversation for global enterprise brands, with millions of dollars being poured into artificial intelligence and machine learning initiatives, data is also extremely useful for small businesses. Even without a huge technology budget, infrastructure, and data storage, small businesses can reap the benefits of utilizing data.
Especially for small businesses, data analytics is used across every single department, from marketing to sales to customer support and finance. Business intelligence software is often one way to aggregate, analyze, and report on data at scale, and when comprehensive data and analytics programs are put in place, companies can reap the benefits.
Why should small businesses use a dedicated data analytics approach?
According to Gartner research, leaning into data and analytics helps improve business growth, efficiency, resilience, and innovation. [1] Small businesses with a dedicated data analytics approach gain a competitive edge over others for benefits like improved decision-making, increased productivity, and more.
Improved decision-making
One of the business benefits of a dedicated data analytic approach is improved business decision-making. Data provides valuable insights about the health of your customers, business, products, and more. Valuable data lives with each of your internal teams. For example, the sales team might have an intricate knowledge of the data around win rates. The marketing team has valuable conversion data, showing what type of messaging performs well. Bringing all of this data together in a unified, cohesive approach helps improve all team’s decision-making. For example, sales and marketing can work together to refine sales pitches and collateral once they see a certain truck is driving higher win rates. Data and analytics teams should champion the end-to-end architecture that makes this collaborative, unbiased approach to decision-making possible.
Increased productivity
While data is incredibly useful by itself, bringing a unified data analytics approach increases internal productivity. When data is easily accessible and shared across teams, individual employees can take charge of their own learning, analyze trends, and discover valuable patterns and insights. With this information in hand, teams can work faster and smarter, focusing on projects and initiatives that matter.
Enhanced customer experiences
One of the best ways to use data analytics in your organization is to improve the overall customer experience. With key insights into customer behavior, preferences, purchasing patterns, and more, not only can you more efficiently target the right potential buyers, but you can craft a more personalized experience for them once they enter your business. Qualitative data like age, gender, location, and more can be used to create personalized sales and marketing content, getting your advertising messages across more effectively. Once they’re a customer, both qualitative and quantitative data can be used to send them sales at the right time, product recommendations, and more based on their past purchasing behavior.
Also, when customers reach out to support, service representatives can answer questions faster and more accurately when they have easy, accessible customer data to analyze. Customers won’t have to repeat themselves, look up order numbers, and other things that might make their support experience frustrating. Overall, a well-thought-out data analytics approach makes a smoother buying journey from beginning to end for customers.
Future-proof your business
As data becomes key to winning in a modern business market, setting up a formal data analytics program today benefits your business for the long term. Having years of historical data is extremely helpful when making significant future business decisions like investments or launching a new product line. Setting up the infrastructure to store, organize, and access your data is also easiest when done from the beginning. As your organization expands and shifts with time, your data analytic strategy supports this growth and can scale with you.
One way to optimize data analytic strategies to future-proof your business is through predictive analytics. Predictive analytics are sophisticated artificial intelligence strategies that work to forecast future outcomes. This might be something like sales projections, growth models, and more. By utilizing historical trends and data, predictive analytics helps future-proof your business by empowering smart decision-making based on unbiased objectives.
Tips for making your data and analytics approach effective
A data and analytics program is not a one-and-done project; it involves continual process optimization, improvements, and maintenance. As your data and analytics function becomes more sophisticated, you can perform routine data cleansing, segmentation, and continued integrations through departments. Take a look at some tips below for making your data and analytics approach effective.
Tip #1: Create a D&A function that is proactive, not reactive
Data and analytic functions are most effective when they are proactive, not reactive. The idea of “thinking like a business” from Gartner’s research report encourages analytics teams to focus on value to the organization instead of individual data sets. Instead of allowing data and analytics teams to simply wait for reporting requests, teams should take the initiative to proactively drive awareness and usage across the organization. Data and analytics teams should champion the use of data by devising a plan to activate the rest of the organization.
Plus, when data and analytics teams are focused on thinking like a business, they’re able to proactively future-proof infrastructure, platforms, integrations, and more. Especially as emerging technologies like artificial intelligence and machine learning take center stage, businesses that take a proactive approach will be able to lean into these initiatives sooner and with more success.
Tip #2: Remove data silos
Not only are siloed departments harmful to the overall business health, but they’re especially detrimental to a strong data analytics program. Data silos are when a certain side of data is controlled by a singular unit, reducing collaboration, transparency, and productivity. When data is isolated, it’s difficult to aggregate large sets of company data to effectively analyze trends and patterns.
Between the marketing team’s customer relationship management system (CRM) to customer support’s ticketing service to accounting budgeting platforms, there are potentially dozens of places data is coming from. When important insights are relegated to a single platform or team, it’s incredibly hard to create a data-driven culture, optimize processes, recognize trends, and get the full advantages of data.
Ideally, a small business ecosystem of data is easily accessible, shareable, and continuously updated in real-time. Instead of operating on single, individual software platforms, bringing everything together in a true data ecosystem is important. As you bring on new tools and platforms, it’s equally important that they can plug into your current business ecosystem without creating a new silo.
Tip #3: Keep employees engaged in the process
While data deals with fax and figures, it’s critical to maintain the human element. Keeping employees engaged in new data analytics processes and standards is one way of obtaining their buying to a data-driven culture. Ideally, employees are trained, onboarded, and shown how to effectively analyze charts and reports. When employees feel empowered and confident to dig into numbers and trends, they’re more likely to proactively discover new takeaways, obtain valuable insights, and take these learnings to their larger team.
Plus, employees are often critical components of maintaining data hygiene as they’re able to spot-check for inaccuracies and notice if something is missing. Your employees might be the first signal that a key integration stopped working or data seems out of date. That’s why it’s important to drive top-down leadership change when it comes to embracing a data-driven culture.
Implement effective data and analytics in your business
For small businesses, a strong data and analytics function provides key benefits when it comes to business growth, resiliency, and competitive advantages. Small businesses don’t need huge data initiatives to make an impact, either. Unified, cohesive data analytics functions can support every team, from marketing to sales to support to leadership.
To scale your approach to an effective data strategy, lean into the benefits of improved decision-making, increased productivity, enhanced customer experiences, and the ability to future-proof your business. To get started, assess where your current software stack stands and begin listing out all the individual platforms where data might live. This is often the first step in removing silos and developing a proactive, business-first approach.
This article was originally published at Softwareadvice.com, by author Katherine McDermott. Original article >>
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