Business Users Guide to Data & Analytics Initiatives

Data-driven organizations are built, in no small part, from dozens (and sometimes dozens more) of individual data and analytics initiatives. These initiatives lead to solutions that empower leaders and teams to achieve business goals with data-driven decisions — at speeds and levels of accuracy that manual analytics or even business acumen can’t rival.

When individuals in departments throughout the organization have secure access to data and analytics, they’re empowered to spot and act quickly on insights and emerging trends. This helps drive business growth and better situates organizations for achieving and retaining competitive advantage.

Data and analytics solutions help organizations accomplish big goals, like improving operational efficiencies, optimizing marketing campaigns, and identifying areas to enhance customer service, or smaller goals that build upon each other to further progress toward larger or broader goals.

In this blog, we lift the curtain on data and analytics initiatives to help business leaders get a clearer picture of:

  • What data and analytics initiatives are
  • Why they are important
  • How leaders can ensure investment in initiatives are successful and achieve ROI
  • The processes involved with implementing data and analytics initiatives
  • How business and IT leaders can access required data skill sets during this time of scarcity


What Are Data and Analytics Initiatives?

Data and analytics initiatives are individual digital transformation projects that include final products, such as slick dashboards, handy visuals, and migrations to the cloud — but their full potential for value extends far beyond these flashy outputs.

Each initiative or “project,” when planned carefully and in line with overarching business goals, contributes to growth and competitive advantage by bringing nontechnical business users more access to digitized, structured data from internal and third-party sources.  


Why Are Data Analytics Initiatives Important?

What leading digital native companies like Facebook and Google have demonstrated all too well is that agility, or the ability to discover and quickly act on emerging information and trends, is key to scaling growth and reaching competitive advantage.

Traditional, “legacy” data and analytic processes often leave business leaders waiting weeks or months as an already-overworked IT team analyzes and reports on individual requests. This creates lag times for learning business intelligence and disables organizations from changing course quickly in response to new trends.  

Many older, more traditional organizations also have executives making decisions based on intuition, opinion, or even internal politics. In fact, a 2021 Talend survey found 36% of executives still rely on intuition to make most of their decisions.

Data-centric capabilities, however, chip away at top-down leadership models by “democratizing” data — that is, by putting reliable information in the hands of more people (executives, managers, and team members) throughout the organization. With more and diverse experimentation and access to information, organizations gain exponential possibilities for innovation.  

And it’s working.

Foundry’s 2022 Data & Analytics Study revealed 63% of business leaders credited data and analytics initiatives for their ability to introduce “new revenue opportunities and/or lines of business” within their organization. And 88% of Information Technology (IT) decision-makers agree that successful initiatives hold potential for “fundamentally chang[ing] the way their company does business.”

Key Ingredients for Successful Data Analytics Initiatives

Successful data analytics initiatives will produce — or make it possible to produce — business value for the organization.

Each initiative and its specific goals will vary widely, and thus, so will the data, processes, and infrastructure they need. However, there are some key ingredients tied to success. These include:

Data

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Of course, analytics can’t happen without data, and data analytics projects are only as successful as the quality, variety, and volume of data that teams can access for your project goals.

In all cases high-quality data is widely recognized as:

  • Available: Ready for analytics and reporting inside of a data warehouse
  • Trustworthy: Relevant, up to date
  • Secure: Compliant with government privacy regulations

Gathering data can be easy, if the initiative’s goal is straightforward and all the data your team needs is structured and parked neatly in a data lake.

But sometimes gathering and preparing data for analysis requires more effort by your data team. This can be the case when large volumes of data are scattered in different “silos,” systems or apps throughout the organization or within third-party platforms.  

There may also be cases in which you haven’t yet amassed the type or volume of data necessary for drawing the statistically significant conclusions your project goals require.

What if I don’t have enough of the data I need?

  1. You might simply begin collecting the data you want. Sometimes it’s possible to use an iterative process that sources data incrementally and allows you to build momentum.
  2. Ask your data team whether it’s possible to connect to third-party sources of data. The Power BI Desktop platform, for instance, allows users to connect to and import data from up to 600 rich data sources, including Google Analytics, Azure Marketplace, Facebook, HDInsight, and more.  
  3. Web scraping is another popular, legal method of extracting publicly available data from websites.
  4. Consider entering a data sharing agreement with another organization in your industry.

Keep in mind that in all cases, the more data sources you want to bring together for analysis, and the deeper your data team must dig for that data, the more complex and time-consuming (and therefore, expensive) your project will be.

Once your team has found the right data sources, they — along with the tools and processes used to collect, integrate, and transform data — will ensure all three criteria for high-quality data are met.

Clear Goals

Business goals for your data-driven initiative should always be linked to your organization’s broad corporate strategy or mission-critical goals.

Sometimes initiative goals are simply milestones along the path to executing a larger strategy, however they can and should always be linked to measurable factors.

Examples of business goals may include:

  • Growing revenue or market share to a certain milestone
  • Maintaining profit margins at a certain percentage point
  • Attracting and keeping talent

A Comprehensive Data Strategy

In line with an organization’s critical, long-term vision and goals, companies sometimes develop comprehensive data strategies. Within these comprehensive strategies, business leaders develop “roadmaps” for deploying individual data and analytics initiatives over time.

Comprehensive data strategies also include planning investments in the people, tools, and infrastructure needed for data-driven success. Often, chief data officers (CDO/CDAOs) are hired to lead this charge. According to the NewVantage Partners Data & AI Leadership Executive Survey (2022), 73.7% of executive respondents have appointed Chief Data or Analytics Officers.  

Without a comprehensive data strategy, business and IT leaders should be extra vigilant about ensuring the goals they ultimately choose will help bring the organization closer to reaching mission-critical business objectives.

A Strong Data Culture

Though comprehensive data strategies as described above will help organizations streamline initiative planning, Peter Drucker’s words still reign: “Culture eats strategy for breakfast.”

Only 26.5% of executives surveyed in the 2022 NewVantage survey mentioned above believe they’ve succeeded at creating data-driven organizations. Of those who haven’t, 91.9% cite culture as “their greatest impediment.”

Clearly, nothing is more important than buy-in from the individual end-users within your organization. If they don’t see the value of using innovative technology, following new processes, or relying on analytical results, your initiative won’t show a successful return on investment (ROI).  

The more leaders and individual team members understand the value of using data and investing in the time and effort it takes to learn new processes and technology, the closer an organization comes to creating a strong data culture.

Entire cultures can be slow to change but thought leaders in data science and the corporate space have found ways to speed up the evolution. Some of these include:

Sponsorship  

Sponsors can spearhead initiatives and educate teams on the value of data-driven decision-making. They can also promote data literacy and training.

Evangelism

Once new data and analytics capabilities begin giving teams highly valuable results in the form of, say, more efficient processes, team members and leaders will be more likely to evangelize and champion further innovation with data and analytics solutions throughout the organization.

Executive Leadership

When CEOs lead by example and give unequivocal support for data-driven decision-making and data literacy, people within the organization are more likely to sit up and take notice. They’re more likely to engage in training opportunities and take other meaningful steps toward using data solutions.

Data Literacy & Training

Often, successful adoption of data initiatives comes down to training and improving levels of data literacy throughout the organization.

Online technical training school, DataCamp, says that “To build organizational data literacy, leaders must embark on transformational programs that provide their workforce with the skills, access, and tools to work with data at scale and transform their data culture.” (“The Complete Guide to Data Literacy | DataCamp”)

Data & Analytics Governance

Business and IT leaders use governance policies to control the quality and dependability of data and help teams find and understand the information they need.  

By assigning controls, such as user access, to data, governance policies help organizations comply with government security and privacy regulations and protect customers, employees, and themselves from data breaches.  

A solid analytical governance framework also helps guide data scientists on structuring ethical models for analytics, machine learning (ML), and artificial intelligence (AI) projects that help them produce reliable results.  

Data Security & Privacy

Data security measures protect sensitive information from unauthorized access and exploitation. Ensuring every data analytics initiative follows compliance requirements can help generate trust with customers, patients, employees, and possibly even boost your appeal to investors.

TechTarget (among many others) describes data security as based on three main principles, a.k.a., the “CIA triad”:

Confidentiality

  • Ensuring only appropriate users have access to data

Integrity

  • Preventing data from being erased or modified by bad actors
  • Transferring data securely

Availability

  • Making sure the data people need is available when they need it

Skilled Data & Analytics Implementation Teams

Whether your data initiative implementation team comes from within your organization, or you decide to outsource, you’ll want to familiarize yourself with the key team members and their roles.

Although teams can be structured in diverse ways, most include data scientists, data analysts, data engineers, and a project manager.

Data Scientists

Data scientists use scientific methods to extract insights from data based on specific business problems. They’re skilled at sourcing quality data, integrating it, and managing large-scale analyses using advanced mathematics, programming, and various tools.

They also create visualizations and use ML to build models that deliver insights to business users.

Data Engineers 

Data engineers build and maintain datasets that can be easily accessed by the rest of the data team. They also design, develop, and code applications for capturing and cleaning data and often standardize data attributes across datasets.

Data Analysts 

Data Analysts perform many of the analyses required by your project and help shape problems for data scientists to explore. Data analysts are often charged with presenting findings to business stakeholders as well.

Project Managers

Project managers are key members of every data and analytics team. They oversee data-driven projects and services, and, through leadership and direction, they ensure data teams meet the intended goals and objectives.

Outsourcing Data & Analytics Projects

Accessing the skilled talent you need for deploying new data initiatives isn’t always easy. In fact, Accenture’s Closing the Data Value Gap study found “more than half of the companies” have a tough time hiring and keeping the skilled talent they need.

Since most data initiatives are time-fixed projects with limited budgets, keeping the right mix of skilled talent on staff isn’t always possible. And asking internal IT teams to step away from their everyday work and devote what could be several weeks to a data initiative usually isn’t practical.

Many organizations find it makes more sense to contract data initiatives out to third-party vendors that specialize in deploying successful, data-driven initiatives. These vendors often have a diverse mix of highly skilled and experienced professional data talent (as described above) available for augmenting internal IT resources, managing data operations, or even helping out on special projects.

When choosing a third-party vendor, make sure to vet your data team well. They should have (or be able to quickly develop), a deep knowledge of your industry domain and have experience deploying similar initiatives.

Critical Steps for Selecting Initiative Goals

  1. Bring All Stakeholders to the Goal-Setting Table

Business Leaders

Business leaders and managers are important drivers of data initiatives. They should help determine whether the initiative goals align with broader corporate goals.

Business leaders can also help evaluate and influence end-user buy-in. As needed, they might institute change management practices, training, help remove impediments to end-user acceptance and success, or all the above.

IT and Data Team

Your initiative’s implementation team can help evaluate whether the project is possible from a data, infrastructure, privacy, and skillset perspective.

Business Users

End users can provide important perspectives on whether, and to what extent, the new analytics initiative will add value to individual teams and departments. Their perspective on usability and intended goals of the initiatives, such as enhanced collaboration, better communication, operational efficiencies, cost-management may also prove invaluable.

  1. Align Goals With Organization’s Mission-Critical Business Goals

To achieve maximum ROI and impact with your initiative, your initiative goals must align with the broader corporate strategy, priorities, and goals.

Forbes agrees and reminds us that choosing an initiative that doesn’t serve your business goals is “akin to burning money in the name of data.”

  1. Evaluate Feasibility of Goals

Stakeholders from business and IT should examine all potential requirements related to the proposed goals to ensure the feasibility of project completion.

Requirements include:

  • The right data, and enough of it
  • The right, and available, skilled talent
  • The right infrastructure in place or you have the budget to buy new tools
  • The intended end users will understand the value and adapt to new processes and technology (If not, leadership is willing to plan for training, data literacy, or even change management assistance.)

Deploying Data & Analytics Initiatives

Hold on now, we’re about to get a little bit technical (but not too much!). Do you need to memorize the following information? No. But when your data team starts talking (especially about what you can or can’t do given the state of your data, infrastructure, and so on), you might find it helpful to refer to a few notes.

Once all stakeholders have vetted the goals of your data initiative, IT will take the lead on deploying the initiative.  

Depending on the scope of your specific initiative and goals, the team may work through each of the following stages, from data processing to dashboard creation, or your needs may only call for work in one or more of them.  

To keep this discussion as simple as possible, we’ll discuss all three stages that a typical data initiative follows from start to finish (business team goes from limited or no access to internal or external data to having access to the data they need and can perform analytics using a dashboard with visualizations).

Stage 1: Data Processing (a.k.a., “Data Integration,” “Data Preparation”)

First, your implementation team will likely need to spend much of their time preparing data. This process includes integrating, cleaning, and imposing a consistent data structure in preparation for the next stage (data modeling).

Here’s a brief run-down of what’s involved with data processing:

ETL/ELT

The two main integration processes used today include ETL and ELT.

Extract-Transform-Load (ETL)

ETL integrates, cleans, structures, and transforms data from one or more sources (e.g., SQL databases, websites, Excel spreadsheets, flat files, SaaS applications, etc.) before loading it into a database, such as a data lake or data warehouse.

ETL is a well-established process that’s considered ideal for transforming smaller sets of complex data in batches. It’s also the preferred method for when data security is a priority. 

Extract-Load-Transform (ELT)

ELT is a newer technology that can allow for more flexibility in the data extraction phase. It differs from ETL primarily with respect to when and where data transformation takes place.    

By performing the transformation phase last, the extract and load phases can happen more quickly, which is ideal for extracting large volumes of streaming data.

Stage 2: Data Modeling

In this stage, IT will use the structured data for creating analytical models, visualizations in dashboards and reports, and enforcing secure access controls.  

Data teams base models on subject areas that best suit project goals. To prepare the models for high-quality results, IT teams may ask business departments questions like:

  • What do you want to learn from your data?  
  • Do you want to see who your top customers are? What are your total sales?
  • How do you want to use data?  
  • Is it for transactional or reporting purposes?
  • How often will you access data?
  • Whether it’s information you need daily or perhaps only once or twice per year will make a difference in the type of data models the team chooses.
  • How much data do you have or need  
  • There’s a big difference between working with megabytes vs. petabytes, for instance.
  • What level of security do you need?  

Stage 3: Data Visualization

Once data modeling is finished, the team will begin work on end products, like visualization and dashboards.

Data visualizations are a powerful and welcome alternative to spending hours making sense out of large data sets (especially for us nontechnical business users!).

With contemporary design standards, visualization allows nontechnical individuals to quickly grasp results of data analytics and present stakeholders with compelling reports, documents, charts, tables, graphs, images, and more.

As IT and business leaders work together to set up models for visualization, they’ll consider key factors, including:

  • The level of detail business users will need for decision-making
  • The graphs and charts that most clearly display this data
  • The best way to lay out the data so it’s easy for end users to quickly interpret 

The Importance of Context

While choosing the right visual elements is important to ensuring end users understand results quickly, it’s also important they understand the context of the data they’re analyzing.

Data may not lie, but statistics can be misleading when examined in the wrong context.

IT and business leaders mitigate this by:

  • Encouraging organization-wide data literacy
  • Including visual design guidelines within dashboards, such as on-screen callouts, that inform users when data was last refreshed
  • Defining key performance indicators (KPIs), measures, and metrics as part of their analytics governance strategy

Stage 4: Dashboards

Dashboards are the slick technology that display collections of visualizations for data and analytics results.  

From the dashboard, users ask questions of data in everyday language, such as "What was the largest gross sale last year?" and receive a visual reply.  

Visual dashboards, reports, and alerts empower data democratization by helping more business users throughout the organization explore data and engage in their own analyses to look for patterns, discover insights, and make fast, informed business decisions.

Conclusion

Data and analytics initiatives are part and parcel of every data-driven organization.  

Organization-wide, data-driven decision-making is how leading organizations spot emerging trends and act on insights faster than competitors.  

Organizations don’t passively “become” data driven. Instead, leaders build data-driven organizations with intention, through individual transformation initiatives. These projects harness and transform business data into products that individuals throughout the organization can use to conduct analyses and make actionable business decisions.  

Initiatives may include small projects or larger ones that prepare data for machine learning ML and AI endeavors. Regardless of their scope, each initiative holds potential for contributing toward data-driven decision-making and successful ROI on a much larger scale than is possible through traditional, legacy processes. But this potential only holds with careful planning, implementation, and use by individuals.  

IT and business department leaders should exhaust all efforts in determining whether achieving an initiative’s goal is possible. End users must also understand and support the goals and be willing to learn how to use new technology and follow new processes.  

This level of collaboration requires a strong commitment to cooperation and communication between executives and all stakeholders, including IT, business department leaders, and individual end users.

These days, progress toward data-driven success is often hampered by the current scarcity of appropriate data-related skill sets. If this is an issue within your organization, consider outsourcing data and analytics work to an experienced third-party team with diverse skill sets.

Wimmer Solutions offers strategic business and IT consulting and implementation services for organizations across industries. Our highly skilled teams are dedicated to helping each organization within our community of clients achieve their business goals.  

Reach out to us today or anytime for support with your data-driven venture.