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Business Intelligence Data Visualization

The 3 Key Pillars to Better Dashboard Design

How you design your dashboard is crucial when it comes to displaying your data effectively. It’s important to visualize your data in a way that’s clear and easy for viewers to understand. However, with the abundance of data and reports needed to answer queries, it can be difficult to know what to consider in your design process. Let’s dive into the three key elements to implement when improving your dashboard design.

1. Develop a Plan

It’s natural to want to play around with your data and jump right into building dashboards. Nevertheless, when beginning, try not to start creating and adding charts right off the bat. It’s useful to plan ahead and layout the details of your dashboard prior to actually constructing it. This means determining the overarching purpose of your dashboard as well as what information needs to be included. Planning ahead will help to minimize overcrowding and continual adjustments to your design later on. 

What Should Go Where?

Thinking about the user’s experience when viewing a dashboard is essential when it comes to deciding where specific information should go. Here are a few things to think about when determining your initial dashboard design plan.

Placement

There is only one thing to be said about placement: location, location, location. While your dashboard is far from the real estate sector, consider that users will naturally give more attention to the left side of the screen. According to a recent eye-tracking study, users spend 80% of their time viewing the left side of the screen and only 20% viewing the right. 

Specifically, users were found to look to the top left corner of the screen the most, making this section of your dashboard prone to increased amounts of attention. The most utilized graphs and metrics should be placed in this portion of your dashboard, or any additional visualizations you deem significant. 

Don’t Hide Things

Similar to the point above regarding placement, you want to prioritize key information and make sure it’s easily found. You can’t expect much work from your end viewer to dive deeper than the surface data presented. Any additional clicking or scrolling required to find information is unlikely to be discovered by viewers. 

All things considered, an easy way to solidify your plan would be to create a rough draft either on paper or in any design application. This will allow you to play around with your placement and take a deeper dive into how certain elements complement each other.

2. Sometimes Less is More

We’ve all heard the common phrase that sometimes “less is more,” and dashboard design is no exception to this philosophy. You want your dashboard to be clear, concise, and easy to read. Avoid including too many charts and any unnecessary information. While an abundance of charts and graphs might appeal to the data driven enthusiast in you, they might be difficult for other viewers to read and understand. Minimizing the amount of data presented will prevent your audience from feeling overwhelmed due to information overload.

Choosing the Right Data Visualization

Choosing the most effective visualization for your data plays a key role in your dashboard’s simplicity. This is dependent on the type of data you are trying to visualize. Are you working with percentages? Data over a specific period of time? Are there any relationships present that you are trying to convey? 

The many variables that make up your data will affect your ultimate choice in visualization. Be sure to consider characteristics such as time, dates, hierarchies, and so on. 

3. Keep the End Viewer in Mind

Your audience is just as critical to your dashboard’s design as the information being presented. It’s important to always keep the end viewer in mind and understand how they are actually using the presented information.

When determining the characteristics of your end viewer, ask yourself questions such as:

  • Who will be viewing this dashboard on a daily basis?
  • How often do my viewers work with the type of data being presented? 
  • How will my audience be viewing this dashboard? Will viewers be sharing it as a pdf?

The answers to these questions will help you determine how much descriptive information to include alongside your visualizations.

Overall, there are numerous elements to consider when it comes to developing your business dashboards. It’s vital to always keep your audience in mind and plan ahead. Consider these key tips next time you’re building a new dashboard for improved design.   

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Big Data Business Intelligence Data Analytics

5 Strategies to Increase User Adoption of Business Intelligence

Companies are turning to new strategies and solutions when it comes to using their data to drive decisions. User adoption is essential to unlocking the value of any new tool, especially in the field of business intelligence. However, like with most things, people are commonly resistant to change and often revert back to their original way of doing things. So how can organizations avoid this problem? Let’s explore five strategies that will help to effectively manage change and increase user adoption of business intelligence. 

Closely Monitor Adoption

It’s no secret that people are hesitant when introducing new tools and processes. If you don’t keep a close eye on the transition to a new tool, users will likely continue to use outdated methods such as disparate and inaccurate spreadsheets. Make sure those involved are working with the solution frequently and in the predetermined capacity. If you notice a few individuals rarely using the tool, reach out to discuss their usage as well as any concerns they might have surrounding the business intelligence solution. 

Top-Down Approach

Another strategy to increase user acceptance is the top-down approach. Buy-in from executives and senior stakeholders is crucial to fostering adoption, whether it be throughout your team or the entire organization. 

Consider bringing on an executive to champion the platform. This will empower other end-users to utilize the tool and recognize its overarching importance to the business moving forward. Leadership should also communicate heavily the why behind moving to a new solution. This will align stakeholders and help them to understand the transition as a whole.  

Continuous Learning & Training

Training is key to the introduction of any new processes or solutions. But you can’t expect your employees to be fully onboarded after one intensive training session. Try approaching the onboarding process as a continuous learning opportunity.

Implement weekly or bi-weekly meetings to allow everyone involved to reflect on what they’ve learned and collectively share their experience. Additionally, allotting time for regular meetings will give people the chance to ask questions and troubleshoot any possible problems they’ve encountered. 

Finding Data that Matters

Demonstrate the power of using data to drive decision making by developing a business use case. This application will allow you to establish the validity of the BI solution and show others where it can contribute value across business units. Seeing critical business questions answered will assist in highlighting the significance of the tool and potentially spark other ideas across users.

Remove Alternatives

A more obvious way to increase adoption is to remove existing reports or tools that users could possibly fall back on. Eliminating alternatives forces users to work with the new solution and ultimately familiarize themselves with the new dashboards.

Conclusion

Overall, there are many effective strategies when it comes to increasing user adoption. The downfall of many companies when it comes to introducing new solutions is their focus on solely the technical side of things. The organizational change and end-user adoption are just as crucial, if not more important, to successful implementation. Consider these approaches next time you’re introducing a new business intelligence solution. 

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Big Data Business Intelligence

ETL vs. ELT: Critical Differences to Know

ETL and ELT are processes for moving data from one system to another. Both processes involve the same 3 steps, Extraction, Transformation, and Loading. The fundamental difference between the two lies in the order in which the data is loaded into the data warehouse and analyzed.

What is ETL?

ETL has been the traditional method for data warehousing and analytics. It is used to synthesize data from more than one source in order to build a data warehouse or data lake. First, the data is extracted from RDBMS source systems, which is the extraction stage. Next is the transformation stage, where all transformations are applied to the extracted data, and only then is it loaded into the end-target system to be analyzed by business intelligence tools.

What is ELT?

ELT involves the same three steps as ETL, but in ELT, the data is loaded immediately after extraction, before the transformation stage. With ELT, all data sources are aggregated into a single, centralized repository. With today’s cloud based data warehouses being scalable and separating storage from compute resources, ELT makes more sense for most modern businesses. ELT allows for unlimited access to all of your data by multiple users at the same time, saving both time and effort.

Benefits of ELT

Simplicity: Transformations in the data warehouse are generally written in SQL, which is the traditional language for most data applications. This means that anyone who knows SQL can contribute to the transformation of the data.

Speed: All of the data is stored in the warehouse and will be available whenever it is needed. Analysts do not have to worry about structuring the data before loading it into the warehouse. 

Self service analytics: When all of your data is linked together in your data warehouse you can then easily use BI tools to drill down from an aggregated summary of the data to the individual values underneath.

Bug Fixes: If you discover any errors in your transformation pipeline, you can simply fix the bug and re-run just the transformations with no harm done. With ETL however, the entire process would need to be redone.

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Big Data Business Intelligence Data Analytics

Relational vs. Multidimensional Databases: Why SQL Can Impair Your Analytics

What is a Relational Database?

A relational database is a type of database that is based on the relational model. The data within a relational database is organized through rows and columns in a two-dimensional format.

The relational database has been used since the early 1970s, and is the most widely used database type due to its ability to maintain data consistency across multiple applications and instances. Relational databases make it easy to be ACID (Atomicity, Consistency, Isolation, Durability) compliant, because of the way that they handle data at a granular level, and the fact that any changes made to the database will be permanent. SQL is the primary language used to communicate with relational databases.

Below is an example of a two dimensional data array. Each axis in the array is a dimension, and each entry within the dimensions is called a position.

Store Location Product 1 Product 2
New York 83 68
London 76 97
As you can see we have an X and a Y axis, with each position corresponding to a Product and a Store Location.

What is a Multidimensional Database?

A multidimensional database is another type of database that is optimized for online analytical processing (OLAP) applications and data warehouses. It is not uncommon to use a relational database to create a multidimensional database.

As the name suggests, multidimensional databases contain arrays of 3 or more dimensions. In a two dimensional database you have rows and columns, represented by X and Y. In a multidimensional database, you have X, Y, Z, etc. depending on the number of dimensions in your data. Below is an example of a 3-Dimensional Data Array represented in a relational table and in 3-D.

Item Store Location Customer Type Quantity
Product 1 New York Public 47
Product 2 New York Private 20
Product 1 London Public 36
Product 2 London Public 69
Product 1 New York Private 36
Product 2 New York Public 48
Product 1 London Private 40
Product 2 London Private 28

The third dimension we incorporated into our data is “Customer Type” which tells us whether our customer was public or private.

We can then add a fourth dimension to our data, which in this example is time. This allows us to keep track of our sales, giving us the ability to see how each product is selling in relation to each store location, customer type, and time.

What are the Advantages and Disadvantages of Relational Databases?

Advantages: 

Single Data Locations: A key benefit to using relational databases is that data is only stored in one location. This means that each department will pull the data from a single collective source, rather than each department having their own record of the same information. This also means that when data is updated by one department, that change is reflected across the entire system, so that everybody’s data is always updated.

Security: Certain tables can be made available only to who needs it, which means more security for sensitive information. For example, it is possible for only the shipping department to have access to client addresses, rather than making that information available tclient addresses, rather than making that information available to all departments.

Disadvantages:

Running queries: When it comes to running queries, the simplicity of relational databases comes to an end. In order to access data, complex joins of many tables may need to be made, and even simple queries may need to be structured in SQL by a professional.

Live System Environments: Running a new query, especially ones that use DELETE, ALTER TABLE, and INSERT, can be incredibly risky when using a live system environment. The slightest error can mess everything up across the entire system, leading to loss of time and productivity.

What are the Advantages and Disadvantages of Multidimensional Databases?

Advantages:

Similar Information is Grouped: All similar information is grouped into a single dimension, keeping things organized and making it easy to view or compare your data.

Speed: Overall, using a multidimensional database will be faster than using a relational database. It may take longer to set up your multidimensional database, but in the long run, it will process data and answer queries faster.

Easy Maintenance: Multidimensional databases are incredibly easy to maintain, due to the fact that data is stored the same way it is viewed: by attribute. 

Better Performance: A multidimensional database will achieve better performance than a relational database with the same data storage requirements. Database tuning allows for further increased performance. Although the database cannot be tuned for every single query, it is significantly easier and cheaper than tuning a relational database.

Disadvantages:

Complexity: Multidimensional databases are more complex, and may require experienced professionals to understand and analyze the data to the fullest extent.

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Big Data Business Intelligence Data Analytics

DataOps 101: Why You Can’t Be Data-Driven Without DataOps

It’s no secret that data is becoming more and more central to every organization. Companies are investing heavily in their IT infrastructure as well as recruiting top talent to maximize every effort in becoming data-driven. However, most companies are still missing one key component from their data initiatives: DataOps.

DataOps isn’t necessarily new, many organizations already possess various elements and processes that fall under the philosophy without knowingly labeling them as DataOps. But many questions come to mind when the topic of DataOps is introduced. What is it? Why is it important? How is it different from the way you’re already working with data? Let’s address these questions and take a deep dive into why DataOps is essential to becoming truly data-driven.

What is DataOps?

While DataOps isn’t confined to one particular definition or process, DataOps is the culmination of many tools and practices in order to produce high-quality insights and deliverables efficiently. In short, the overarching goal is to increase the velocity of analytics outcomes in any particular organization while also fostering collaboration. Similar to DevOps, it’s built on the foundation of taking an iterative approach to working with data.

Why is DataOps Important?

In today’s fast-paced business climate, the quicker you can respond to changing situations and make an informed decision the better. The end-to-end process, though, when working with data can be quite extensive for many data science teams. Having systems in place to decrease the amount of time spent working with data anywhere in the process from data prep to modeling can promote operational efficiency. This improves the use of data to drive decisions across teams and the organization as a whole.

Furthermore, DataOps is all about improving how you approach data, especially with the high volumes of data being created today. This enhanced focus when working with data can lead to:

  • Better decision making
  • Improved efficiency
  • Faster time to insights
  • Increased time for experimentation
  • Stronger data-driven culture

Maximizing Time and Resources

Companies have an abundance of data to work with, but extracting value from it first requires data scientists to perform many mundane but necessary tasks in the pipeline. Finding and cleaning data is notorious for taking up too much time. The 80/20 Rule of Data Science indicates that analysts spend around 80% of their time sourcing and preparing their data for use, leaving only around 20% of their time for actual analysis. Once the data has been prepped, data scientists will then model and test before deployment. Those insights then need to be refined and communicated to stakeholders, often through the use of visualization tools.

This brief description of the analytics lifecycle is not entirely exhaustive as well, there are many additional steps that go into orchestration. But with no centralized processes in place, it’s likely that these tasks aren’t being performed in the most efficient way possible, making time to insights a lengthier cycle overall. The main point here is to emphasize the importance of DataOps in maximizing available time and resources. Adding automation and streamlining these tasks can increase your overall analytics agility.

Unifying Business Units

Additionally, DataOps helps unify seemingly disconnected business units and the organization as a whole. Having centralized practices and robust automation allows for less division or infrastructure gaps amongst teams. This can lead to greater creativity and innovation across business units when it comes to working with analytics.

Conclusion

There’s no question that the business value of data can be transformative to an organization. You don’t need to hire a whole new team, chances are you already have the core players needed to realize DataOps in your current operations. DataOps is more about producing these data and analytics deliverables quickly and effectively, increasing operational efficiency overall. If you’re serious about becoming data-driven, you should start thinking about adding DataOps to your data management strategy.

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Big Data Business Intelligence Data Analytics Data Preparation

How to Cure Your Company’s Spreadsheet Addiction

The use of spreadsheets in business today is essential for the vast majority of people. From quick calculations and generating reports to basic data entry, spreadsheets have a way of working themselves into our daily tasks. What’s not to love about spreadsheets? They’re quick, easy to use, require little to no training, and are quite powerful tools overall. However, developing too much of a dependency on them can be problematic, especially when used as a workaround for other solutions due to convenience.

This dependency problem is commonly referred to as spreadsheet addiction. While referring to this phenomenon as an addiction might seem a bit extreme, many organizations find themselves heavily reliant on the use of individual spreadsheets to perform core functions. This high usage rate causes many problems and can ultimately hinder a company’s growth. Let’s explore the potential causes of this addiction as well as review possible treatment plans of action.

What’s Wrong with Using Spreadsheets?

While Excel and Google Sheets can be quite effective in their own right, heavy reliance on spreadsheets can create risk and cause a number of negative effects. 

A few examples of potential problems they create are:

  • Things Start to Break – As the size of the dataset increases, things within your spreadsheet inevitably start to break. Once this starts to occur, it can be seemingly impossible to identify the source of the problem. You’ll likely drain more time and resources into finding and fixing the issue than on your actual analysis. These breaking points also create the risk for errors and other data corruption.
  • Static and Outdated Information – Exporting data from your CRM or ERP system instantly causes the information to become outdated. Spreadsheets don’t allow you to work with your data in real-time, additionally, it’s also extremely difficult to implement any form of automation within your sheets. This creates more work for users as well as poses the problem of inaccuracy.
  • Impedes Decision Making – Spreadsheets are notoriously riddled with errors, which can be costly when it comes to decision making. You wouldn’t want to base any kind of decision on a forecast that is more likely to be inaccurate than not. Reducing discrepancies, specifically human error will improve decision making overall.

Treatment Method to Spreadsheet Addiction

Regardless of the severity of your company’s spreadsheet dependency, effective treatment is no small feat. Change doesn’t happen overnight and you should approach your treatment plan as an iterative process. While this list is not exhaustive, here are a few pillars to consider when promoting change.

Evaluation of Symptoms

First, you must identify how spreadsheets are currently being used. It’s important to start with a comprehensive overview of your situation in order to form an effective plan of action. 

To access your addiction level, start by asking yourself questions such as:

  • How are spreadsheets used by an individual user and across departments?
  • What company processes involve the use of spreadsheets?
  • How are spreadsheets used as a method of collaboration?

Drill down how spreadsheets are being used from the company level down to the individual users. Determine not only how they are being used by departments but also their frequency, purpose, and role in daily operations.

Assign Ownership

Next, assign an individual to lead or build a small team to take ownership of the project. If no one feels they are directly responsible for driving change, the bystander effect will inevitably rear its ugly head on the situation. Assigning responsibility for the transition away from spreadsheets will facilitate the flow of change.

New Solutions and Systems

A new solution requires a widespread change to business processes and will ultimately impact day to day operations. This is the main reason spreadsheet addiction is prolonged in businesses, everyone is more comfortable with their usual way of doing things. But implementing the proper tools and systems is vital to decreasing dependency on spreadsheets. 

Use your evaluation to identify your company’s business intelligence needs and acquire tools accordingly. While this transition might require an initial upfront cost, investing in the proper data and analytics tools will help reduce costs in terms of efficiency and labor in the long run.

Promote User Buy-In 

Buy-in from employees across all levels is crucial to the acceptance of new solutions and business processes. Spreadsheet addiction will prevail if users aren’t comfortable with using the new systems put into place. Learning is required when it comes to any change, it’s essential to offer training and available support resources to aid the shift.

In the end, accept that there will always be some tasks and projects done through Excel or Google Sheets. The important thing is that not everything or even the majority of work will be done through these platforms. Though beating spreadsheet addiction might come with some withdrawals, driving change now will foster greater efficiency in the long run. 

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Big Data Business Intelligence Data Analytics Data Preparation

Why Data Warehouse Projects Fail

As organizations move towards becoming more data-driven, the use of data warehouses has become increasingly prevalent. While this transition requires companies to invest immense amounts of time and money, many projects continue to fail. Let’s take a look at the most common reasons why data warehouse projects fail and how you can avoid them. 

There’s No Clear Big Picture

In most cases, these projects don’t fail due to technical challenges. While there might be some obstacles when it comes to loading and connecting data, the leading pitfalls of project failure are predominantly organizational. Stakeholders commonly feel that there is a lack of clarity surrounding the warehouses’ goals and primary objectives.

Companies often see this most prevalently in the division between technical teams and the ultimate end user. You don’t want your architect or engineers to be on a different page than your analysts. Therefore, it’s important to establish the high-level goals behind why you are undertaking this project to all members of your team before putting processes into place. 

Before beginning, the team should have definitive answers to questions like:

  • What are our data goals?
  • What insights are we looking for to satisfy our business needs?
  • What types of questions do we need the data to answer?

Developing a clear understanding of the big picture early on will help you avoid uncertainty around strategy, resource selection, and designing processes. Knowing the company’s “why” behind taking on the initiative will also allow those involved to recognize the purpose of their efforts.

The Heavy Load of Actually Loading the Data 

Despite the organizational obstacles, there are also many hurdles on the technical side of things. Before data can be loaded into the warehouse, it has to be prepped and properly cleaned. This poses an initial challenge as cleaning data is notoriously a time-consuming task. IT leaders are often frustrated by the wasted hours spent preparing data to be loaded.

The primary main concern is the ability of organizations to easily move and integrate their data. Movement and ease of access to data are crucial in order to generate any kind of insights or business value. According to a recent study conducted by Vanson Bourne and SnapLogic, 88% of IT decision-makers experience problems when it comes to loading data into their data warehouse. 

The most common data loading inhibitors were found to be:

  1. Legacy Systems – Migrating data from legacy technology can be time-consuming. However, the primary issue here is that these systems can be difficult to access, making any kind of data movement restrictive.
  2. Unstructured and Semi-Structured Data – Complex data types are tough to manage in any situation. Inconsistencies surrounding structure and formatting drains time and technical resources, preventing effective loading.
  3. Data Siloed in Different Infrastructures – Disconnection of data sources prevents integration across the organization. Many companies have hundreds of separate data sources as they continually grow across departments and with the addition of various projects. 
  4. Resistance to Sharing Data Across Departments – Oftentimes departments act as their own separate entities and aren’t willing to share. The sales team may not want finance to have access to their customer data due to misaligned goals. 

All of these warehouse factors drain an organization’s time and resources, contributing to a lengthier and more costly project overall. Additionally, improperly loading data can cause a number of problems in itself such as errors and data duplication.

Low End User Acceptance

So you’ve successfully moved your data into the warehouse, now what? Another issue that commonly contributes to the failure of data warehouse projects is end user acceptance. As much as new technologies can be exciting, people are inevitably creatures of habit and might not always delve into acceptance. This is where education and training come into play. Onboarding users is vital to the success of any project. 

Establishing a data-driven culture is the first step to promoting user acceptance and engagement. End users should be encouraged to indulge in their data curiosities. Implementing a form of self-service analytics will increase the ease of use for non-technical users and help them quickly gain access to information. These transitional efforts will not only help with the success and use of your data warehouse but also drive better decision making throughout the organization in the long run.

Conclusion

Overall, there are a variety of reasons that contribute to the failure of data warehouse projects. Whether those pitfalls are organizational or on the technical side of things, there are proven ways to properly address them in order to maximize investment and foster successful insights. 

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Big Data Business Intelligence

Don’t Download Your Data to an Excel Spreadsheet

As concerns surrounding data breaches rise amongst IT leaders, security is at the forefront of every company’s operations. Due to the high cost that comes with these breaches, many businesses spend millions to strengthen their defenses. 

Despite all of the human capital and monetary resources dedicated to protecting data, though, many basic data security risks are commonly overlooked. One of the biggest being downloading data to spreadsheets and Excel files. Let’s take a look at the problems behind this security risk as well as its potential impacts.

What’s the Problem?

Downloading your data to an Excel spreadsheet seems simple enough, chances are you’ve done it yourself on multiple occasions. You might have been quickly searching for answers about last quarter’s sales or current inventory levels. Whatever the case, downloading data, particularly to a personal computer, can cause a number of problems.

The task might seem trivial or insignificant in comparison to other security threats, but what are the real implications of downloading data to a spreadsheet?

Data downloaded to a spreadsheet results in problems such as:

  • The inability to monitor or control how the data is used or shared
  • Files become subject to misuse and exploitation
  • An increased risk of hacking and exposure of confidential information 

Non-Security Issues

Beyond the increased exposure to data breaches, there are many other implications. To begin, you’re unable to work with the data in real-time. Data is constantly changing and once the data is removed from the warehouse, it instantly becomes outdated. You don’t want to be making important decisions based on data from last week if the data is shifting every hour. Working with only a mere snapshot in time could create the wrong picture as well as issues of inaccuracy.

What are the Real Costs?

While the potential risk factors might be evident, let’s review what some of the quantifiable costs are. According to a recent study by IBM, the average cost of a data breach in 2020 is approximately $3.86 million, this number rising significantly each year.

Companies are no stranger to these high costs, many have experienced their own security breaches as a result of spreadsheets. In organizations with hundreds or even thousands of employees, human error is inevitable. In 2014, for example, an employee at Willis North America accidentally sent a spreadsheet containing private information to 4,830 employees enrolled in the company’s medical rewards plan. The attachment contained employees’ names, birthdates, Social Security numbers, employee ID numbers, and additional confidential data. 

The insurance broker had to pay for identity theft protection services for all affected employees as a result, costing them thousands. Additionally, the company received a citation from the US Health Insurance Portability and Accountability Act (HIPAA). Costs, though, extend beyond fines and additional losses, the company’s reputation is also an expense to keep in mind. 

Keep it in the Warehouse

While it’s crucial to take the necessary precautions when it comes to data security, all of those efforts could be undermined by something as simple as a spreadsheet. Data is vulnerable when it comes to movement outside of your data warehouse. Making efforts to minimize this risk is key to preventing data breaches, or else it could cost you. 

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Big Data Business Intelligence

5 Essential Steps to Transform Data into Decisions

Working with all of the data in the world provides no value if the insights gained aren’t used to drive decision-making. If you’re interested in building a more data-centric culture within your organization, follow these 5 steps when transforming your data into decisions.

Figure Out What Data Must Be Produced

Every business has specific questions that need to be answered to grow and improve performance. For example, a business might be experiencing high levels of customer churn because its products aren’t connecting with their current audience. Using available data, the company’s analysts may determine, for example, that the churn is occurring because they are targeting the wrong age group or geographic region. 

In this scenario, the big decision that needs to be made is how to target buyers who will become long-term customers. Making that decision, however, starts with figuring out what data is needed. In this case, an analysis of customer churn will ultimately drive decision-making.

Identifying Potential Data Sources

The raw materials for a project come from the data sets you have access to. If you don’t have the necessary data, processes should first be put in place to collect it. In the previous example, the company might want to acquire data by:

  • Reviewing marketing data
  • Collecting information from sales reports
  • Asking customers to conduct surveys
  • Studying customer service interactions
  • Looking at social media posts

How to Properly Target Your Analysis

Especially with a problem such as customer churn, it’s important to figure out what the sentiments toward the products are. There’s a gap between well-targeted buyers who end up frustrated due to issues with customer service, for example, and buyers who made a one-time purchase because there was a killer discount or seasonal trend. 

Detailed sentiment analysis from multiple data sources can shed light on which groups most of your customers fall into. You might find that the previously targeted customers fell into 5 different categories, and a majority of the churn occurred only in one or two groups. You can then re-evaluate the marketing resources and retargeting efforts to those specified groups, adjusting strategy accordingly.

Different problems will predictably require different forms of analysis. While an issue like customer churn might lend itself to sentiment analysis, a problem like evaluating drug efficacy based on clinical trials may lend itself more to Bayesian inference. It’s important to understand why a particular statistical model might be more relevant than another before moving ahead with analysis.

Producing Insights Rapidly

Decision-making requires the delivery of insights in a timely manner. With analysis in hand, you need to quickly produce deliverables that will be presented to decision-makers. This means thinking about things like:

  • What sorts of reports to write
  • How charts and graphs may be integrated
  • What formats, such as dashboards, PowerPoint presentations, and white papers, should be used to provide insights
  • Who should receive the insights

It’s also important that the delivery of insights becomes a continual and constant process. Teams should be routinely working on projects, and there should be a strong emphasis on producing deliverables.

Driving Actions

Insights needed to be delivered to the right people. There’s no need, for example, to deliver actionable information that a purchasing agent needs to the company’s CEO. You want the fewest steps between insights and frontline decision-makers as possible. 

A data-driven company will make sure that purchasing agents have access to things such as real-time dashboards that show exactly what is trending, how inventory is holding up, and what items have the best margins. With the right processes in place, frontline decision-makers can log in to the system and see fresh insights daily.

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Big Data Business Intelligence Data Analytics

Why Companies Fail to Become Data-Driven

Investing in becoming a data-driven company is a common thread in discussions about the future of business. Unfortunately, a 2018 survey of major corporate executives found that the adoption of a data-driven culture is still lagging at many organizations.

Why are companies failing on data strategy? Let’s look at four reasons why and how they might apply to your situation.

Not Knowing How to Attack Multiple Opportunities

The lack of a solid data-driven culture is one of the biggest problems. This cuts to the core concept of setting the agenda. While a majority of the businesses from the study understood the importance of using analytics to make better decisions, a decided minority were taking advantage of secondary opportunities. For example, barely more than a quarter of the businesses had bothered to test the waters on monetizing their data.

Many companies don’t see data as a direct driver of profits. To this end, companies should consider things like:

  • Creating data-driven products, such as white papers and industry reports
  • Using data to drive interest in social media
  • Selling data directly to third parties

Focusing on Buzzwords Rather Than Action

Even major enterprises with respectable reputations have managed to fumble their “digital transformations.” While announcing a digital transformation is a good way to create a tech-savvy image and boost the share price, it’s not remotely the same thing as formulating a data strategy. 

To get the job done, you need to look at the following issues:

  • Setting internal standards for the acquisition, use, storage, and sharing of data
  • Installing C-level data officers and giving them the power to implement changes
  • Bringing all members of your company on board with the idea of transforming digital operations
  • Hiring professionals with backgrounds in programming, database management, AI, machine learning, and other technologies
  • Providing severance packages for employees and corporate officers who can’t get on board.

It’s important to take action rather than be the company that purely talks about data. A data-centric enterprise has an opportunity to improve processes, employee and customer relations, products, and services. Commit upfront to the process, and you’ll be amazed by the results.

Not Following on Successes with More Efforts

There’s a major risk that any digital transformation effort that fosters a data-centric culture will stall out due to its own success. Be wary of calling any effort quits without laying the groundwork for further successes. The job gets achieved, and possibly quite well, but then data becomes a complete project that fades into the past. 

A company can quickly become the proverbial hare that gets overtaken by the tortoise. While your operation might have done amazing work sprinting out to lead amongst competitors, slow and steady businesses that keep coming with new efforts will always win the race.

Struggling to Establish a Two-Way Street

People at all levels of the organization have to be able to communicate with each other. It’s also a smart idea to ensure that different departments are cooperating in ways that:

  • Prevent duplicating efforts
  • Offer options in an easy-to-understand way
  • Make the most of visualizations
  • Help parties understand what the data is saying.

Not Following the Data to Logical Conclusions

You can read about, listen to, and even develop some of the most valuable data in the world. But it’s important to try to see what the data pool is pointing to. This requires:

  • Experience working with math, data, stats, and probabilities
  • The ability to rapidly read small contexts while thinking macro 
  • Disclosure of all possible biases

Getting to the data-powered future is going to have more than its fair share of bumps. The important thing is to put data-driven shifts at the top of your To-Do list.

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