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Category: Data Analytics

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Data Analytics Education

The Future of K-12 Analytics

Secondary education is a students’ last stop before either entering the workforce or continuing to higher education. Regardless of whichever path they choose, it is crucial to ensure thorough preparation for professional success. Using predictive analytics can increase a student’s likelihood of achieving this success and help continually improve upon their learning experience.

How is Data Analytics Being Used?

Primary and secondary education share many analytics use cases when it comes to improving student outcomes. Both are required to meet criteria based on standardized testing, English language learner proficiency, and additional nonacademic measures. However, secondary education places a much heavier weight on graduation and completion rates. Let’s explore how data is being used to influence the path to completion and advancing these student outcomes.

Attendance

Data analytics can be used to closely examine factors beyond grades such as attendance and the amount of time spent outside the classroom. Schools often have hundreds or even thousands of students, which can make identifying absence trends of individual students challenging. 

Attendance and out-of-school suspension metrics, for example, can be monitored to highlight chronic absenteeism and potential at-risk students. This allows educators to decipher what factors might be supporting or hindering individual students. Reviewing this aggregate data can also bring determinants not typically associated with attendance, such as school climate, to light.

Graduation Rates

Graduation is the goal of every educator and student in secondary education. Though, pinpointing the advancing indicators that a student might drop out can be extremely difficult. Based on historical data, analytics tools can detect complex patterns and insights into signs a student might be in danger of not graduating. Recognizing initial warning signs and taking action early on can make all the difference in the long run.

Curriculum Adjustments

Continuously evaluating and improving upon instruction is another way data analytics is changing secondary education. Curriculum differences amongst feeder schools are an area of concern when it comes to a student’s success within secondary education. For example, say a nontraditional math course is offered at the traditional feeder school to align with the high school’s curriculum. If this course is not offered at the other middle schools in the area, this could position students from other areas to struggle with this topic at the high school level.

Data analytics would enable educators to monitor the performance of this target student group and highlight which students need additional support. This not only assists in keeping students on track with their peers but also maximizes student learning opportunities. 

Conclusion

Big data is transforming the education sector through an increased focus on data-driven decision-making. Promoting these variables of student achievement is a fraction of the core benefits that come with adopting analytics in education. By taking a data-driven approach, any school can enhance student outcomes through actionable insights.

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Big Data Data Analytics Data Quality

What is Data Integrity & Why is it Important in Data Analytics

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What is Data Integrity?

Data integrity is the measure of accuracy, consistency, and completeness of an organization’s data. This also includes the level of trust the organization places on its data’s validity and veracity throughout its entire life cycle.

As a core component of data management and data security, data integrity revolves around who has access to the data, who is able to make changes, how it’s collected, inputted, transferred, and ultimately how it’s maintained over the course of its life.

Companies are subject to guidelines and regulations from governing organizations such as the GDPR to maintain certain data integrity best practices. Requirements are particularly critical for companies in the healthcare and pharmaceutical industry but remain important to decision-making across all sectors. 

Why is Data Integrity Important?

Data integrity is important for a number of reasons, key factors include:

  • Data Reliability & Accuracy – Reliable and accurate data is key to driving effective decision-making. This also assists employees in establishing trust and confidence in their data when making pivotal business decisions.
  • Improving Reusability – Data integrity is important to ensure the current and future use of an organization’s data. Data can be more easily tracked, discovered, and reused when strong integrity is maintained.
  • Minimizing Risks – Maintaining a high level of integrity can also minimize the dangers and common risks associated with compromised data. This includes things such as the loss or alteration of sensitive data.

Risks of Data Integrity

If data integrity is important to mitigating risks, what risks are involved? 

Many companies struggle with challenges that can weaken one’s data integrity and cause additional inefficiencies. Some of the most common risks to be aware of are the following:

  • Human Error – Mistakes are bound to happen, whether they be intentional or unintentional. These errors can occur when proper standards are not followed, if the information is recorded or inputted incorrectly, or in the process of transferring data between systems. While this list is not exhaustive, all of these are able to put the integrity of an organization’s data at risk.
  • Transfer Errors – Transferring data from one location to another is no small task, leaving room for possible errors during the transfer process. This process can result in altering the data and other table inaccuracies.
  • Hardware Problems – Though technology has come a long way by the means of hardware, compromised hardware still poses a risk to data integrity. Compromised hardware can cause problems such as limited access to data or loss of the data entirely.

Data Integrity vs. Data Quality

Are data integrity and data quality the same thing? No, despite their similar definitions and joint focus on data accuracy and consistency, data integrity and data quality are not one and the same.

Data quality is merely one component of data integrity as a whole. Integrity stems beyond whether the data is both accurate and reliable and instead also governs how data is recorded, stored, transferred, and so on. This extension of components, particularly when it comes to the additional context surrounding the data’s lifespan, is where the primary distinction between the two lies.

To sum up, data integrity plays a deciding role in ensuring accurate data that can be easily discovered, maintained, and traced back to its original data source.

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Data Analytics Education

How Data Analytics is Transforming Student Achievement

As the combined use of technology becoming more prevalent in education, the volume of this data has been rapidly increasing along with it. States collect information regarding learning, testing, and demographics from hundreds of students and schools each year. But how exactly are school districts supposed to use all of this data?

To explore how data analytics is transforming primary education, let’s take a look at how it’s currently being used to enhance the key variables of student achievement.

Why Primary Education?

The mission of primary education is to provide students with foundational learning skills and to ultimately promote student success. Along with this mission, it’s also important to keep in mind that primary education consists of a student’s core developmental years. Their success here is critical in preparing them for their journey into secondary education and beyond.

If assessment and student success are not properly monitored at this stage, learning gaps can easily be overlooked. This can have a negative impact on their foundational learning as well as their future achievement outcomes. 

How is Data Analytics Being Used in Education?

Data analytics tools help schools use their data to satisfy state-mandated accountability requirements and identify areas for internal improvement. One of the key functions of Big Data and analytics in education is measuring and providing insights for the various determinants of student achievement. 

While many important factors go into a student’s performance, educators are working with data to improve assessments, ESSA status, and teaching effectiveness.

Assessments

Combining various sources of student assessment data helps teachers and administrators measure performance on multiple levels. Schools can set and monitor education goals for an entire school, a specific class, an individual student, or even by subject. Additionally, the use of these metrics goes beyond the tracking value to administrators. Making assessment and success metrics visible to students also opens up the possibility for students to develop skills in monitoring their individual learning. 

Teaching Effectiveness

Teachers can use this collected data to gain a deeper understanding of how they should tailor future assignments or adapt their teaching style.

Comparing historical assessment data can assist teachers in identifying any possible learning gaps. These insights can then be used to evaluate the design of lesson planning and teaching methods. For instance, a teacher might decide to allocate more time to topics that students have historically struggled with or try a new instructional approach.

ESSA Status

The Every Student Succeeds Act, referred to as ESSA, requires that schools meet a certain degree of academic performance and assigns them a status based on the identified need for support. Accountability here is predominantly focused on the requirements for subgroups of students and other academic measures.

Data analytics empowers schools to convey the performance of these subgroups in real-time. This increases accessibility to measured criteria for both educators and administrators. Schools can then easily communicate this information to stakeholders to not only inform but also spark additional conversations regarding areas of needed improvement. 

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Big Data Data Analytics

The Data-Driven Difference Between Applying & Consuming Data

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Organizations across the board have recognized the significance of using data to drive decision-making and grow their operations. 94% of enterprises say data and analytics are important to their business growth and decision-making process. 

Due to the fundamental role analytics plays in enterprises today, demand for the presence of data in any and all business activities has also developed. Dashboards and reports have become an essential aspect of meetings and day-to-day operations. Whether it be used to address broader strategic problems or to support upcoming project decisions, there is an ever-present need for data to be involved in some capacity. 

However, just because graphs and data visualizations have become the new standard in the workplace doesn’t necessarily mean companies are actually applying the information. The presence of data does not automatically equate to being data-driven. This leads us to the all-important question: Are you applying data or just consuming it?

Using Data to Fit a Narrative

To begin, the problem that’s holding companies back from becoming truly data-driven is that many use data to fit a narrative. More often than not data is used as a means of providing evidence to support predetermined conclusions. This means centering data efforts around backing up ideas or gut feelings rather than focusing on what the data actually tells.

Coming to conclusions before exploring the data can be a recipe for disaster. However, this occurs in businesses today more often than you’d think. Everyone has their own agenda as well as objectives and goals they are responsible for hitting. Even though business leaders are able to recognize the importance of data, the data might not always align with their plan of action. 

Not Putting Biases to the Test

Similarly, not putting these biases to the test is another obstacle holding businesses back from maximizing the value of their analytics. Ronald Coase, the renowned British economist, once said “if you torture the data long enough, it will confess anything.” This quote describes the ease of manipulating data and imposing personal biases, whether they be intentional or unintentional, on the process of data analysis. 

While intuition is important in business, being data-driven is about putting those biases to the test, exploring the data, diving deeper than the surface, and uncovering insights that may have not been considered otherwise. 

How to Become Data-Driven

So how do you make the most of your data? What does it take to become data-driven? Being data-driven doesn’t mean solely investing in the newest data analytics tools or having the highest quality data possible. A data-driven culture is what allows your data to guide you, with the help of technology and governance, in the right direction. An organization’s culture is where the divide between consuming data versus actually applying it comes into play. Here are some key steps to keep in mind when on the path to becoming data-driven.

Improve Accessibility to Data

The initial core element of becoming data-driven is having readily available access to quality data that can be used for analysis. After all, how can any value be derived from your data if no one is able to access the information they need in a timely and efficient manner? Or worse, if the data still needs to be cleansed prior to use. These are all factors that impact the ease of use and flexibility when it comes to using data to drive decisions. Implementing a robust data governance strategy is the key to maintaining the quality and accessibility of your data.

To access your data’s accessibility and current governance strategy, start by asking the following questions:

  • How do you manage and store your data?
  • How do you access your company’s data?
  • Who has access to the data?
  • What metrics are you using to measure data quality?

Build Data Literacy 

Furthermore, data can’t produce any type of meaningful value if no one in your organization is able to understand it. Provide training and opportunities for all employees, beyond the data science and analytics teams, to develop their understanding of how to read, interpret, and analyze data. This will allow for more fluid communication and accessibility to insights across every department.

Promote Exploration & Curiosity

For data to have a meaningful impact on business decision-making, teams have to be willing to probe the data and continually ask it questions. Not every issue or insight can be seen from the surface, deep dives and exploration are required to uncover information that may have not been discovered with basic analysis. Implementing a weekly brainstorming discussion or providing access to further educational training can lead to better engagement amongst employees as well as higher quality insights.  

Communicate High-Level Goals 

Communication of high-level goals is critical to understanding what the organization is trying to achieve through these changes. It’s important to foster a common understanding of how data should be used and prioritized in the broader scope of the company’s goals. This will not only ensure everyone is on the same page, but it will also communicate the business value of data to those involved. 

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Big Data Data Analytics Data Quality

Why We Build Data Warehouses

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What is a Data Warehouse?

A data warehouse is where an organization stores all of its data collected from disparate sources and various business systems in one centralized source. This aggregation of data allows for easy analysis and reporting with the ultimate end goal of making informed business decisions.

While data from multiple sources is stored within the warehouse, data warehouses remain separate from operational and transactional systems. Data flows from these systems and is cleansed through the ETL process before entering the warehouse. This ensures the data, regardless of its source, is in the same format which improves the overall quality of data used for analysis as a result.

There are many additional advantages to implementing a data warehouse. Some key benefits of data warehouses include the following:

  • Enhanced business intelligence and reporting capabilities
  • Improved standardization and consistency of data
  • Centralized storage increases accessibility to data
  • Better performance across systems
  • Reduced cost of data management

Why is a Data Warehouse Important?

Data warehouses are important in that they increase flexible access to data as well as provide a centralized location for data from disparate sources.

With the rapidly increasing amounts of operational data being created each day, finding the data you need is half the battle. You’re likely using multiple applications and collecting data from a number of sources. Each of these sources recording data in its own unique format.

Say you want to figure out why you sold a higher volume of goods in one region compared to another last quarter. Traditionally, you would need to find data from your sales, marketing, and ERP systems. But how can you be certain this information is up to date? Do you have access to each of these individual sources? How can you bring this data together in order to even begin analyzing it?

These questions depict how a simple query can quickly become an increasingly time consuming and complex process without the proper infrastructure. Data warehouses allow you to review and analyze all of this data in one unified place, developing a single source of data truth in your organization. A single query engine is able to present data from multiple sources, making accessibility to data from disparate sources increasingly flexible.

Why We Build Data Warehouses

At the end of the day, data warehouses help companies answer questions. What types of employees are hitting their sales targets? Which customer demographics are most likely to cancel their subscription? Why are we selling more through partnerships and affiliates compared to email marketing? 

Questions like these arise by the handful throughout the course of everyday business. Companies need to be able to answer these questions fast in order to quickly respond to change. Data warehouses empower businesses with the answers they need, when they need them.

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Big Data Business Intelligence Data Analytics

Data Storytelling: The Essential Skill for the Future of Analytics

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Collecting data and performing analysis doesn’t mean much if you can’t find a way to effectively convey its meaning to an audience. Oftentimes, audience members aren’t well-positioned to understand analysis or to critically think about its implications. To engage with an audience, you need to embrace storytelling. Let’s take a look at what that means when talking about storytelling with data.

How to Build a Story Arc

One of the simplest ways to approach the problem is to treat your story as a three-act play. That means your story will have:

  • An introduction
  • A middle
  • A conclusion

Each section of the story needs to be delineated so the audience understands the structure and the promise of a story that comes with it.

What Goes into an Introduction

In most cases, data is hidden before being subjected to analysis. That means you have to set the scene, giving the audience a sense of why the data is hidden and where it came from. You don’t necessarily want to jump right to conclusions about the data or even any basic assumptions. Instead, the data should be depicted as something of a mysterious character being introduced.

If the storytelling medium is entirely visual, then you need to find a way to present the data. The Minard Map is a classic example of how to do this. It uses data to tell the story of the slow destruction of Napoleon’s army during the invasion of Russia. Minard employs a handful of vital statistics to explain what’s going to happen as the story unfolds. These include the:

  • Sizes of the competing armies
  • The geographic proximity of the two forces 
  • Air temperature
  • Rainfall

The audience can familiarize themselves with the data quickly and easily understand what this story is going to entail just by reading the vital statistics. In this particular case, this story is going to be about man versus the elements.

Unfolding the Middle of the Story

Following the presentation of the story should guide the audience toward the conclusion. In the case of the Minard Map, the middle of the story is about a slowly shrinking French army and a slowly growing Russian army that tracks the French. Military engagements occur, and the weather starts to turn. Geographic elements are worked into the graph, too, as the armies cross rivers and march into towns.

Providing the Conclusion

A well-executed data visualization should let the audience get to the conclusion without much prodding. The Minard Map makes its point without beating the audience over the head. By the third act, it’s clear that the conditions have turned and the Russians are now close to matching the French in manpower. As the two armies reach Moscow, it’s clear that what started as a triumphant march has ended as an immense loss.

In its best form, data storytelling shouldn’t feel like a sea of numbers at all. People have seen numerous charts and graphs in their lifetimes, even over the regular course of a single day of business, and that means good-enough visualizations that are focused on presenting numbers tend to become white noise.

Takeaways

Good data storytellers make history. Florence Nightingale’s analysis of casualties during the Crimean War permanently changed the way all forms of medical treatment are provided. Her work is still required reading at many nursing and medical schools more than 150 years later. That’s the goal: to engage the audience so thoroughly that the story and the data long outlast your initial presentation.

Accomplishing that goal requires planning. You can’t just fire up your best data visualization software, import some info from Excel and let the bars and bubbles fly. That’s easy to do because many software packages can deliver solid-looking results in a matter of minutes.

Top-quality data storytelling occurs when the audience is given just enough information to set and understand the scene. Someone scanning the visualizations will then follow the information as it unfolds over time. As the audience approaches the conclusion, they should be left with a strong impression regarding what the data says and what they should learn from it.

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Big Data Data Analytics Data Quality

The Fundamentals of Mastering Metadata Management

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Poor data quality is estimated to cost organizations an average of $12.8 million per year. All methods of data governance are vital to combating this rising expense. While metadata has always been recognized as a critical aspect of an organization’s data governance strategy, it’s never attracted as much attention as flashy buzzwords such as artificial intelligence or augmented analytics. Metadata has previously been viewed as boring but inarguably essential. With the increasing complexity of data volumes, though, metadata management is now on the rise. 

According to Gartner’s recent predictions for 2024, organizations that use active metadata to enrich their data will reduce time to integrated data by 50% and increase the productivity of their data teams by 20%. Let’s take a deeper look into the importance of metadata management and its critical factors for an organization.

What is Metadata?

Metadata is data that summarizes information about other data. In even shorter terms, metadata is data about other data. While this might sound like some form of data inception, metadata is vital to an organization’s understanding of the data itself and the ease of search when looking for specific information. 

Think of metadata as the answer to the who, what, when, where, and why behind an organization’s data. When was this data created? Where did this data come from? Who is using this data? Why are we continuing to store this information?

There are many types of metadata, these are helpful when it comes to searching for information through various key identifiers. The two primary forms of metadata include:

  • Structural – This form of metadata refers to how the information is structured and organized. Structural metadata is key to determining the relationship between components and how they are stored.
  • Descriptive – This is the type of data that presents detailed information on the contents of data. If you were looking for a particular book or research paper, for example, this would be information details such as the title, author name, and published date. Descriptive metadata is the data that’s used to search and locate desired resources.
  • Administrative – Administrative metadata’s purpose is to help determine how the data should be managed. This metadata details the technical aspects that assist in managing the data. This form of data will indicate things such as file type, how it was created, and who has access to it. 

What is Metadata Management?

Metadata management is how metadata and its various forms are managed through processes, administrative rules, and systems to improve the efficiency and accessibility of information. This form of management is what allows data to easily be tracked and defined across organizations.

Why is Metadata Management Important?

Data is becoming increasingly complex with the continually rising volumes of information today. This complexity highlights the need for robust data governance practices in order to maximize the value of data assets and minimize risks associated with organizational efficiency.

Metadata management is significant to any data governance strategy for a number of reasons, key benefits of implementing metadata processes include:

  • Lowered costs associated with managing data
  • Increases ease of access and discovery of specific data
  • Better understanding of data lineage and data heritage
  • Faster data integration and IT productivity

Where is this data coming from?

Show me the data! Not only does metadata management assist with data discovery, but it also helps companies determine the source of their data and where it ultimately came from. Metadata also makes tracking of alterations and changes to data easier to see. Altering sourcing strategies or individual tables can have significant impacts on reports created downstream. When using data to drive a major company decision or a new strategy, executives are inevitably going to ask where the numbers are coming from. Metadata management is what directs the breadcrumb trail back to the source. 

With hundreds of reports and data volumes constantly increasing, it can be extremely difficult to locate this type of information amongst what seems to be an organizational sea of data. Without the proper tools and management practices in place, answering these types of questions can seem like searching for the data needle in a haystack. This illuminates the importance of metadata management in an organization’s data governance strategy.

Metadata Management vs. Master Data Management

This practice of managing data is not to be confused with Master Data Management. The two have similar end goals in mind when it comes to improving the capability and administration of digital assets. But managing data is not all one and the same, the practices are different through their approaches and structural goals. Master data management is more technically weighted to streamline the integration of data systems while metadata management focuses on simplifying the use and access of data across systems.

Overview

Metadata management is by no means new to the data landscape. Each organization’s use case of metadata will vary and evolve over time but the point of proper management remains the same. With greater data volumes being collected by companies than ever before, metadata is becoming more and more critical to managing data in an organized and structured way, hence its rising importance to one’s data management strategy.

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Big Data Business Intelligence Data Analytics

3 Things Companies Are Doing Right Now to Bounce Back

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The past year has been filled with a long list of unforeseen challenges for all organizations. This uncertainty has left businesses with more and more questions that need to be answered, particularly when it comes to their strategies moving forward. 

There’s no question that data and analytics are paving the way for businesses in the post-pandemic world, but what exactly are companies doing right now to bounce back? Let’s take a closer look at what leading companies are doing to get back on track and prepare for growth in a post-COVID world.

Investing in the Digital Future

There has always been an enormous amount of talk surrounding the need for digital transformation. Organizations have long used the buzzword loosely when discussing their strategy and high-level goals. For many years, there has been a recognized need for these digital efforts but the transformation has been slow to move. The recent market disruptions, though, have catalyzed digital transformation and emphasized the importance of cost optimization and process improvement through new digital strategies. 

Industry leaders are doubling down on their investment in digital strategy and IT implementation. According to a recent Gartner survey, 69% of board of directors have accelerated their digital business initiatives due to COVID’s disruption. This has caused an increase in “forward-looking investments” that will aid in quick responses to unexpected events. Through widespread digital transformation, there has been an apparent shift towards preparedness and the improved agility of organizations as a whole. 

Additionally, digital transformation opens doors for things like customer engagement due to increased customer visibility and opportunities for personalization. Many companies are entirely transforming their business model while expanding their digital product offerings as a means for revenue growth. 

To start accessing your own digital strategy, start by asking questions such as:

  • How is your digital investment aligned with your business goals?
  • What metrics and KPIs need to be tracked to effectively measure change?
  • What strategies and high-level goals should be understood at all levels of the organization?

Data-Driven Decision Making

Decisions, decisions, decisions. The one thing you can always count on to remain constant in business, regardless of any change and uncertainty in the business environment. The road following a disruption, though, is filled with new questions surrounding customer behaviors and strategic decisions that need to be made quickly. 

The recent marketplace changes have highlighted the need for rapid decision-making. Rapid decision-making falls into both an organization’s preparedness and ability to adapt to evolving situations. Using data to drive and inform decision-making is no longer considered a competitive advantage, but instead is identified as a need in order to compete. Whether you’re predicting staff and inventory requirements or evaluating online buying behaviors through customer analytics, data should be at the core.

Not Putting Things Off

Arguably the most important thing companies are doing to bounce back from the downturn is taking action. The time is now, stop putting things off! The vast majority of companies deferred a number of initiatives and business development efforts due to COVID. You’ve likely heard it used as an excuse to delay or revisit a project in the future yourself. 

Nevertheless, progress or effective change never came from putting something on the back burner. You can’t expect growth to happen on its own and continue to delay efforts until things go back to what was once considered normal. Reevaluate your priorities, preparedness, and strategies from the individual level all the way up to the overarching organization. 

Review

To sum up, the points stated above, the key to moving forward is all about adapting to changing situations. Whether it’s your ability to quickly generate insights that will drive decision-making or investing in new digital channels, it all comes back to how prepared you are to respond and adapt to change.

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Big Data Data Analytics

7 Bad Habits Every Data Scientist Should Avoid

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Are you making these mistakes? As a data scientist, it can be easy to fall into some common traps. Let’s take a look at the most common bad habits amongst data scientists and some solutions on how to avoid them. 

1. Not Understanding the Problem

Ironically, for many data scientists, understanding the problem at hand is the problem itself. The confusion here often occurs for a couple of reasons. Either there is a disconnect between the data scientist’s perspective and the business context of the situation or the instructions given are very vague and ambiguous. These reasons all lead back to a lack of information and understanding of the situation.

Misunderstandings of the business case can lead to wasted time spent working towards the wrong approach and often causes many unnecessary headaches. Don’t be afraid to ask clarifying questions, having a clear picture of the business problem being asked is vital to your efficiency and effectiveness as a data scientist. 

2. Not Getting to Know Your Data

We’re all guilty of wanting to jump right in and get the ball rolling, especially when it comes to a shiny new project. This ties into the last behavioral point, rushing to model your data without fully understanding its contents can create numerous problems in itself. A thorough and precise exploration of the data prior to analysis can help determine the best approach to solving the overarching problem. As tempting as it may be, it’s important to walk before you can run.  

After all, whatever happened to taking things slow? Allocate time for yourself early on to conduct an initial deep dive. Don’t skip over the getting to know you phase and jump right into bed with the first model you see fit. It might seem counterintuitive but taking time to get to know your data at the beginning can help save time and increase your efficiency later down the line. 

3. Overcomplicating Your Model

Undoubtedly, you will face numerous challenges as a data scientist, but you will quickly learn that a fancy and complicated model is not a one size fits all solution. It’s common for a complex model to be a data scientists’ first choice when diving into a new project. The bad habit, in this case, is starting with the most complex model when a more simple solution is available. 

Try starting with the most basic approach to a problem and expand your model from there. Don’t overcomplicate things, you could be causing yourself an additional headache with the time drained into the more intricate solution.

4. Going Straight for the Black Box Model

What’s worse than diving in headfirst with an overly complex model? Diving in headfirst with a complex model you don’t entirely understand. 

Typically, a black box is what a data scientist uses to deliver outputs or deliverables without any knowledge of how the algorithm or model actually works. This happens more often than one might think. Though this may be able to produce effective deliverables, it can also lead to increased risk and additional problems. Therefore, you should always be able to answer the question of “what’s in the box?” 

5. Always Going Where No One Has Gone Before

Unlike the famous Star Trek line, you don’t always have to boldly go where no man has gone before in the realm of data science. While being explorative and naturally curious when it comes to the data is key to your success, you will save a lot of time and energy in some cases by working off of what’s already been done.

Not every model or hypothesis has to be a groundbreaking, one of a kind idea. Work from methods and models that other leaders have seen success with. Chances are that the business questions you’re asking your data or the model you’re attempting to build have been done before. 

Try reading case studies or blog posts speaking on the implementation of specific data science projects. Becoming familiar with established methods can also give you inspiration for an entirely new approach or lead you to ideas surrounding process improvement.

6. Doing It All Yourself

It’s easy to get caught up in your own world of projects and responsibilities. It’s important, though, to make the most of the resources available to you. This includes your team and others at your organization. Even your professional network is at your disposal when it comes to collecting feedback and gaining different perspectives. 

If you find yourself stuck on a particular problem, don’t hesitate to involve key stakeholders or those around you. You could be missing out on additional information that will help you to better address the business question at hand. You’re part of a team for a reason, don’t always try to go it alone!

7. Not Explaining Your Methods

The back end of data science projects might be completely foreign to the executive you’re working within marketing or sales. However, this doesn’t mean you should just brush over your assumptions and process to these non-technical stakeholders. You need to be able to explain how you got from point A to point B, how you built your model, and how you ultimately produced your final insights in a way that anyone can understand.

Communication is essential to ensure the business value is understood and properly addressed from a technical standpoint. Though it might be difficult to break things down in a way that non-technical stakeholders can understand, it’s important to the overall success of any project you will work on. This is where storytelling tactics and visualizations can come in handy and easily allow you to communicate your methods.

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BI Best Practices Business Intelligence Data Analytics

How to Transform Your Lazy BI Team

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Are lazy BI practices getting the best of your team? It’s not uncommon for teams to become entrenched in their usual way of doing things, particularly when repeatable and seemingly mundane tasks are involved. There is always an opportunity for growth and process improvement in any team, but a case of lazy BI can make any new methods or change difficult to implement. 

If you’re looking to ignite change, don’t worry, best practices are always here to lend a hand with everything from data sources to server management. Whether you’re dealing with the self-taught BI wizard of the team or just a tired coworker, here are some strategies that can help. 

If You’re New to the Team

Before we jump into strategies, if you’re in the unique situation of being new to the team, there are a few things you should keep in mind. Though it might be easier for you to see where improvements need to be made as an outside source, it’s important to establish rapport with your teammates before rushing to make changes. 

Begin by observing and make note of potential adjustments to workflows or processes. Additionally, be inquisitive and ask questions to figure out the why behind methods that aren’t considered to be best practice. After you’ve allowed some time to get a feel for the entirety of the situation, consider these methods when developing your approach. 

Why Change if Nothing’s Broken?

Why should you do things differently if your current methods are getting the job done? Don’t be surprised if you receive the ‘if it ain’t broke, don’t fix it’ mentality in response. This is a common and natural resistance to changes proposed, the way you go about bringing people on board is essential.

Use the Laziness to Your Advantage

It’s not uncommon for new processes or best practices to be swept under the rug following their initial introduction. While new ways of doing things might be more efficient and a good idea on paper, no change can survive without successful adoption from the majority.

The key is appealing to less work and effort exerted in the future. Even though it might take time to adjust and create increased work for your team initially, it’s important to emphasize the mass amounts of time they will save in the future.

In this approach, you’ll be responding to the age-old question of “what’s in it for me?” 

Though best practices are better for productivity and the organization as a whole, how will these changes directly benefit those involved? Appealing to the desire at an individual level will increase your chances of successful implementation.

Start Small

The key to any kind of change is to start small. Upheaving the old methods to make way for new ones is extremely disruptive and can be overwhelming to most. Starting small will increase your chances of a successful adoption.

Find something small your team can achieve or begin to change. This is the same philosophy people use when altering their life by doing something as simple as making your bed each day. Though it is a small task, it helps those involved to feel accomplished. This makes one more likely to be productive elsewhere in their day as well as being open to greater change.

Conclusion

Overall, it’s important to remember that there is no ironclad rule or gold standard for the successful adoption of new methods. There is no absolute anecdote or cure to a case of lazy BI. Regardless of which strategies or tactics you’re using to influence change, every team is different in the way they learn and adapt. Each scenario bears its own unique characteristics in terms of behavior, environment, and the topic of change itself. As a leader and a teammate, it’s up to you to access these factors and strategize your approach accordingly.

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