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What Is A Data-Driven Strategy?

When a company employs a “data-driven” approach, it makes strategic decisions based on data analysis and interpretation.

Seventy-seven percent of CEOs reported that they planned to pursue operational efficiencies to increase revenue in 2019.

A disciplined and comprehensive embrace of data will be essential to meeting this goal.

A data-driven approach enables companies to examine and organize their data to serve their customers and consumers better.

By using business insights and real-time information, organizations can develop a data-driven strategy that outlines a plan of action to improve front or back-office operations.

This data-driven decision-making gives companies a competitive advantage

It equips management with detailed insights, such as customer data, which allows them to personalize experiences while meeting short and long-term goals.

Why Do Companies Struggle Using Data?

For one thing, traditional companies tend to have trouble corralling data into usable and actionable intelligence.

In our experience, common reasons are siloed data, poor data reliability, and a lack of analytical talent.

Incompatible legacy systems can also impede companies. Different types of data may exist in different formats across an organization, making them hard to compare or combine reliably.

Why Are Data-Driven Decisions Important?

A data-driven strategy is for collecting and analyzing data. It demands that your organization take a systematic approach.

Data-driven strategies help companies make better business decisions by interpreting big data and analyses.

A business strategy may seek to improve supply chain stages, such as production and order fulfillment efficiency, or enhance customer satisfaction by innovating customized experiences.

Glancing at the sales numbers or looking over a website analytics set isn’t enough. Without a systematic approach to analyzing the data, it’s easy to give in to unseen biases or assumptions.

Why Is Data-Driven Decision-Making Better Than Gut Instinct?

Following a data-driven approach offers measurable advantages. A data-driven strategy uses facts and hard information rather than gut instinct.

Using a data-driven approach makes it easier to be objective about decisions. The results of your data analysis can tell you whether it would be wise to follow a particular course of action.

A data-driven strategy involves more than looking at numbers. Many decision-makers base their decisions on theory, instinct, tradition, or gut feeling.

Sometimes, they use a few numbers to back up these decisions.

It isn’t easy to transition from a company that makes decisions based on gut instinct or historical data, to a truly data-driven strategy company.

To do so requires the right kulture and supporting technology. Outdated kultures, siloed business units, and legacy systems are significant blockers.

Other challenges include poor-quality internal data and a lack of data analysts.

However, businesses can – and do – become data-driven if they embrace information as a transformational platform for the entire organization, top-down.

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Choosing The Right Data

The universe of data and modeling has changed vastly over the past few years.

The volume of information snowballs, while opportunities to expand insights by combining data are accelerating.

Bigger and better data give companies more panoramic and more granular views of their business environment.

Seeing what was previously invisible improves operations, customer experiences, and strategy.

Don't Silo Your Data

Before integration solutions and big data, companies categorized much of enterprise data into silos assigned to specific departments.

This lack of information sharing limits internal communication, making it difficult for organizations to monitor metrics from each data source.

If your organization is operating in silos, data isn’t being freely shared. It’s necessary to break down those silos and open up communication.

All decision-makers across the organization need to access data. A data-driven culture needs a single, unified source of accurate and trustworthy data.

Create a central internal resource that explains how and where to find the data. That includes the content, format, and structure of your databases.

This is essential to promote decision-making based on data.

What Are Data-Driven Strategies Used For?

A data-driven sales strategy is a method of data collection of all sales interactions with potential and existing customers to enhance the sales team’s performance.

In other words, the data-driven sales approach implies gathering all the stored information on your product and sales activities and transforming it into unlooked-for insights.

What Are Data Analytics?

Data-driven organizations find value through data analytics, which analyzes data to acquire business insights.

The data can then help add business value, like solving business problems or improving processes.

The value of data enables business leaders to make informed decisions that can enhance business performance, streamline operations, and stronger customer relationships.

A data-driven approach means leveraging complex content analytics and search analytics. Only large operations had access to powerful data analytics platforms in years past.

Accelerating change and potential disruption have led many organizations to adopt a proactive approach. 

Predictive analytics has become central to the organization’s planning, performance, and competitive advantage.

But new advances in data search capabilities make the power of big data much more accessible. 

Now, businesses of any size can collect, analyze, and access data to unlock their insights.

Data analytics involves analyzing data ( big data in particular) to conclude.

Cutting Through The Fog of Business Opportunities

Data-driven strategy streamlines the entire process of market research. It makes your efforts more informed and more powerful.

You can leverage data to detect emerging threats and changes in the industry. That allows you to adapt faster.

It also helps you identify emerging trends in the marketplace before competitors do. Data helps you capitalize on new business opportunities more quickly.

Balancing local solutions with cloud-based facilities can be a practical way forward to help deliver relevant insights to where they are needed, especially if data consumers are geographically dispersed.

Another consideration will be making data easily usable by technologies such as artificial intelligence (AI), machine learning, and augmented reality.

Such tools could play a critical role in generating more nuanced insights and integrating data into operations in new ways.

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Encourage Open Communication

Many initial big data and analytics implementations fail because they aren’t in sync with a company’s day-to-day processes and decision-making norms.

Model designers need to understand the types of business judgments managers make to align their actions with broader company goals.

Department managers can make informed decisions based on up-to-the-minute information. They can analyze data to find ways to decrease expenses and reduce waste.

They can also drive increased value from existing assets.

Everything about a data-driven approach starts with collecting high-quality data. You must also make sure that it is accessible to everyone who needs to decide.

It’s necessary to break down those silos and open up communication.

A financial executive may want to see what the data says about the bottom line. A marketing manager may want to see which marketing campaigns worked and which ones failed and why.

The gap between data analysis and decision-making requires sharing insights in the decision-maker’s preferred language.

The data should also be easily accessible via user-friendly dashboards where leaders can revisit it as needed.

Confidence In Business Decisions

Data-driven insights replace objective opinions with rational facts. This kind of deep analysis provides the clarity you can’t get from intuition or opinion alone.

It lets you benchmark your existing performance and develop a clear path to reach your goals.

Build Data Models

Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes.

More important, the most effective approach to building a model usually starts not with the data, but with identifying a business opportunity and determining how the model can improve performance.

Kotter's Strategic Model

This eight-step model can help you prepare for impending change and chart your change journey, including employee involvement for success.

Conducting these analyses will provide you with valuable insights to develop the needed techniques and strategies to leverage change for your organization and enable transformation.

Optimize The Sales Funnel

A sales funnel is the process a person completes to become a company’s customer.

Within this process, businesses can discover metrics regarding traffic, leads, and sales, allowing management to determine how to increase customer loyalty, sales, and profits.

Choosing The Right Data Tools

Technology choices will have a significant bearing on organizations’ ability to exploit data strategically and operationally — enabling them to unleash data from legacy silos and integrate it with other data sources to create something useful and meaningful.

Data must be in a form that can join other enterprise data. There are many options, such as relational databases, NoSQL stores, or Hadoop.

Use the right tool for the job. For instance, the financial analysts at Warby Parker have been using Excel to compute the key metrics reported to senior management for a long while.

They sucked down huge amounts of raw data from different sources and ran VLOOKUPS (an Excel function to find cross-references in the data) to join all that data to get a top-level look at the numbers.

This worked well initially, but as the company’s sales and customers grew, they needed to adapt.

Using Data Analysis To Hit Your Business Goals

An impactful strategy should be based on business objectives rather than technological initiatives.

While technology helps to generate reports, management should be the ones to develop strategies and make final decisions.

The data should be secure, accurate, and up to date to ensure its relevance to the specific operation at hand.

Companies can encourage a more comprehensive look at data by being specific about the business problems and opportunities they need to address.

Managers also need to get creative about the potential of external and new sources of data.

Social media generates terabytes of nontraditional, unstructured data in conversations, photos, and videos.

Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to weather forecasts.

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Processing Raw Data

As technology grows and expands to every aspect of business life, more data channels are introduced and waiting to be discovered.

For companies with automated software, system processes, such as inventory control services, hold a wide range of raw data to be processed and used for actionable insights.

Information on inventory turnover rates, peaks in demand, and prices can be monitored so management can find what actions can help save capital and time.

The main thing at this stage is to ensure that whoever is in charge of this important task has all the essential tools and knowledge to complete it.

When assigning data handling to an in-house team, you also have to remember the data quality. The impact of low-quality sales data is dramatic.

You risk wasting your team’s time without getting anything in return, losing large sums of money on inefficient decisions, and even harming your company’s reputation by reaching out to the wrong people.

Distribute Data For Hiring Staff

Hire the right talent with data development and analysis skills, to lead this internal innovation program.

Some businesses may need to develop their people, and others will need to hire.

Emphasize open communication and transparency (i.e., breakdown silos between data scientists/analyst and business managers and their teams)

Rework the organizational structure if necessary so that data science isn’t kept central and siloed. Instead, embed these roles into every business department.

To avoid any possible mistakes during data collection due to miscommunication between different departments, you need to assign this task to someone.

In most organizations, the IT department is in charge of data gathering, recording, presenting, and storing.

Bring data to life with rich insights and dashboard reporting, sharing this information widely across the organization to all who are motivated and enthused to see it.

Integrating Data

Implementing integrated systems allows all departments to work in unison through streamlined communication and the exchange of company data.

Project management teams can easily access relevant data and develop plans of action to improve their operation’s functionality.

Becoming data-driven requires companies to collect and examine reports from every internal operation to create an effective process improvement plan.

They can do more research to dig up case studies of how other organizations tackled similar business problems through analytics.

Leadership and board sponsorship of data programs are critical.

Managers of the data engineers can assign resources to data integration and quality so that data is trusted. 

Senior managers can promote or demand greater data sharing and designate clearer ownership and stewardship of data, such as appointing a chief analytics officer or chief data officer.

To maximize its impact, data should not be restricted to managers. Rather, the goal should be to get good data out to the point of need, and into the hands of those at the front line.

And to truly embed data into mainstream thinking and behavior, organizations need to look at how they can build data insights into business processes by default.

Accurate Predictions

Data-driven approaches help organizations discover powerful insights hidden in their data. It gives them the ability to test different business strategies with increasing accuracy.

The results of that testing can improve future predictions. Mining data in near real-time can help organizations minimize the consequences of missteps. It also helps improve recovery time.

Improve Business Outcomes

Another primary goal of collecting data is gaining insight into future outcomes using predictive models such as forecasting software.

For example, demand forecasting solutions take real-time quantities from inventory and point-of-sale (POS) systems to monitor customer behavior and predict future item demand.

Embedding Analytics

Managers need transparent methods for using the new models and algorithms daily.

Terabytes of data and sophisticated modeling are required to sharpen marketing, risk management, and operations.

The key is to separate the statistics experts and software developers from the managers who use the data-driven insights.

The goal is to give frontline managers intuitive tools and interfaces that help them with their jobs.

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Make A Feedback Loop

A feedback loop provides insights into whether something is working or not.

For example, in a customer feedback loop, customer feedback (input) influences business decisions on what changes will go into the next version of a product (output).

In a data-driven organization, business leaders who provide feedback on their effectiveness measure the implementation and use of data.

The Bottom Line

Big data and analytics have climbed to the top of the corporate agenda.

Together, they promise to transform the way companies do business, delivering the kind of performance gains last seen in the 1990s, when organizations redesigned their core processes.

And as data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.

Keep collecting data and track your progress. The sales data collection process never ends.

Once you start it, you have to keep searching for more intelligent and thoughtful facts to improve your team’s operations each time.

Thus, you can define what time and day of the week work best to send sales emails, how many interactions it takes to set up a discovery call, etc.

Hop-On The Kennected Train

According to HubSpot, 61% of companies name their top marketing challenge is generating traffic and leads.

If you’re part of that 61%, keep reading because what you’re about to discover could be a game-changer for you and your business (like it has for thousands of people before you).

So many business owners & sales professionals that come to us are struggling to get ahead because they’re stuck relying on referrals, lead lists, or manual prospecting.

This is taking up too much of their time and isn’t creating the consistent income they’re looking for to fund the lifestyle they want for themselves and their families.

Four years ago, the founders of Kennected had the same struggles. They found that lead generation was way too complicated, expensive, and time-consuming, knowing there had to be a better way.

So they developed a new way of getting qualified leads that turn into new customers on the #1 platform for B2B lead generation… LinkedIn.

In the four years since we developed this lead gen strategy, we’ve earned a spot on the Inc. 5,000 list of Fastest Growing Companies in America.

And more importantly, it helped over 17,000 businesses get new leads and sales for THEIR businesses.

Want to see how it works?

Click here to see how you can use Kennected to fill your sales pipeline with qualified leads in less than 10 minutes per day.

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