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.
In the wake of the Big Data age, everyone seems to be talking about data. Data is at the center when it comes to industry news, board meetings, and almost every strategy or new project moving forward. Even job descriptions for non-traditionally data-focused roles are looking for candidates with the ‘data-driven mindset.’ As a result, the way we do business is rapidly evolving and it’s clear that data is here to stay.
Despite all of the talk and enthusiasm surrounding data, though, what are organizations doing with this newfound data-driven focus? How do you go about actually transforming data into actionable insights? How do you determine the right approach when analyzing your data?
There are a number of techniques and methods to choose from when analyzing your data. In this post, we’ll explore a few of the most common and effective data analysis methodologies to help you maximize your approach when working with data.
1. Regression Analysis
Regression analysis is the statistical process of estimating relationships between one dependent variable and one or more independent variables. The focus here is on determining which variables could have a possible impact on the chosen dependent variable. The most common end goal of regression analysis is to identify patterns and predict future trends.
It’s important to note that there are multiple forms of regression analysis, each varying based on the type of data being analyzed and the nature of the variables involved. Overall, regression models remain an effective way to highlight casual relationships and make inferences about those relationships.
2. Monte Carlo Simulation
The Monte Carlo simulation, also known as the Monte Carlo method, is a mathematical technique used to evaluate the probability of certain outcomes and events occurring. Through random sampling and specified parameters, the simulation can be run repeatedly to produce a thorough range of probable results. The more times the simulation is run, the more accurate the range of possibilities will likely be. This methodology is particularly useful when assessing potential risks and to aid the decision-making process.
3. Data Mining
Data mining is an interdisciplinary field that combines a number of machine learning and statistical processes. There are many different techniques that fall under the data mining umbrella, from data preparation to clustering and classification. It is also all about identifying patterns amongst large sets of data from multiple sources to generate new insights. The end goal, though, is to identify areas of improvement, opportunity, and optimize costs.
To learn the major elements and stages of data mining, also read: What is Data Mining?
4. Sentiment Analysis
Sentiment analysis, also referred to as opinion mining or emotional AI, focuses on the analysis of qualitative data. Sentiment analysis is the combination of text analysis, natural language processing, and other computational techniques to determine the attitude or opinions behind data. This method helps analysts easily determine whether the response or viewpoint on a topic is positive, negative, or neutral. Companies commonly use this form of analysis to determine customer satisfaction levels and access their brand reputation. Data collection can be achieved through informal channels such as product reviews or mentions on social media.
Hypothesis testing is a statistical approach that allows analysts to test assumptions against the parameters of their chosen population. Through testing sample data one can determine the probability that their hypothesis is correct. This method is helpful in making predictions on the effects of decisions before they’ve been made. For example, say you have a theory that increasing your advertising spend will lead to higher sales. Hypothesis testing would allow you to test the validity of your claim, based on your previous sales data or data collected through a generation process, to make a more informed decision. Choices that seem obvious or guaranteed to succeed might not have the desired effect you’d think. This makes the importance of testing and validating your claims all the more important to avoid costly mistakes.
We create data every day, oftentimes without even realizing it. To put a number on it, it’s estimated that each day we create 2.5 quintillion bytes of data worldwide. Tasks as simple as sending a text message, submitting a job application, or streaming your favorite TV show are all included in this daily total. However, not all of this data is created equal.
Similar to the many unique ways there are to create data, there is also a corresponding array of various data types. Data types are important in determining how the data is ultimately measured and used to make assumptions.
Let’s get down to the fundamentals of numeric data types as we explore discrete data, continuous data, and their importance when it comes to Big Data and analytics.
Numeric Data Types
Numerical data types, or quantitative data, is what people typically think of when they hear the word “data.” Numerical data types express information in the form of numbers and assign numerical meaning to data. There are two primary types of numerical data: discrete and continuous data.
What is Discrete Data?
Discrete data also referred to as discrete values, is data that only takes certain values. Commonly in the form of whole numbers or integers, this is data that can be counted and has a finite number of values. These values must be able to fall within certain classifications and are unable to be broken down into smaller parts.
Some examples of discrete data would include:
The number of employees in your department
The number of new customers you signed on last quarter
The number of products currently held in inventory
All of these examples detail a distinct and separate value that can be counted and assigned a fixed numerical value.
What is Continuous Data?
Continuous data refers to data that can be measured. This data has values that are not fixed and have an infinite number of possible values. These measurements can also be broken down into smaller individual parts.
Some examples of continuous data would include:
The height or weight of a person
The daily temperature in your city
The amount of time needed to complete a task or project
These examples portray data that can be placed on a continuum. The values can be continually measured at any point in time or placed within a range of values. The distinguishing factor being that the values are measured over time rather than fixed.
Continuous data is commonly displayed in visualizations such as histograms due to the element of variable change over time.
Discrete Data vs. Continuous Data
Discrete and continuous data are commonly confused with one another due to their similarities as numerical data types. The primary difference, though, between discrete and continuous data is that discrete data is a finite value that can be counted whereas continuous data has an infinite number of possible values that can be measured.
If you’re questioning whether or not you’re working with discrete or continuous data, try asking yourself questions such as:
Can these values be counted?
Can these values be measured?
Can these values be broken down into smaller parts and still make sense?
The Importance of Numerical Data Types
Discrete and continuous data both play a vital role in data exploration and analysis. Though it is easy to review definitions and straightforward examples, data is often filled with a mixture of data types. Making the need to be able to identify data types all the more important.
Additionally, many exploratory methods and analytical approaches only work with specific data types. For this reason, being able to determine the nature of your data will make handling your data more manageable and effective when it comes to yielding timely insights.
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.
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.
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.
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.
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.
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.
In today’s business world, it seems like all decisions and strategies ultimately point back to one thing: data. However, how that data is being used to find value and produce insights from within the data stack is a different story. Business intelligence and data science are two terms often used interchangeably when talking about the who, what, why, and how of working with data.
While they both appear to work with data to solve problems and drive decision-making, what’s the real difference between the two? Let’s get back to the basics by diving into the similarities and differences of each when it comes to their core functions, deliverables, and overall role as it relates to data-driven decision-making.
What is Business Intelligence?
Business intelligence is developing and communicating strategic insights based on available business information to support decision-making. The purpose of business intelligence is to provide a clear understanding of an organization’s current and historical data. When BI was first introduced in the early 1960s, it was designed as a method of communicating information across business units. Since then, BI has evolved into advanced practices of data analysis but communication has remained at its core.
Additionally, BI is much more than processes and methods for analyzing data or answering specific business questions, it also includes the technologies behind those methods. These tools, often self-service, allow users to quickly visualize and understand business information.
Why is Business Intelligence Important?
Since data volumes are rapidly increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This gives guidance towards informed decision-making and identifying areas of improvement, leading to greater organizational efficiency and an increased bottom line.
What is Data Science?
While there is no universally accepted definition of data science, it’s generally accepted as a field that embraces many disciplines, including statistics, advanced programming skills, and machine learning, in order to generate actionable insights from raw data.
In simple terms, data science is the process of obtaining value from a company’s data, usually to solve complex problems. It’s important to note that data science is still developing as a field and this definition is continually evolving with time.
Why is Data Science Important?
Data science is a guide through which companies are able to predict, prepare, and optimize their operations. Moreover, data science can be pivotal to the user experience, for many businesses data science is what allows them to offer personalized and tailored services. For instance, streaming services, such as Netflix and Hulu, are able to recommend entertainment options based on the user’s previous viewing history and taste preferences. Subscribers spend less time searching for what to watch and are able to easily find value amongst the hundreds of offerings, giving them a unique and personally curated experience. This is significant in that it increases customer retention while also enhancing the subscriber’s ease of use.
Business Intelligence vs. Data Science: What’s the Difference?
Generally speaking, business intelligence and data science both play a key role in producing any organization’s actionable insights. So where exactly is the line between the two? When does business intelligence end and data science begin?
BI and data science vary in a number of ways, from the type of data they’re working with to project deliverables and approaches. See the figure below for a visual distinction between the most common attributes of the two.
Perspective
Business intelligence is focused on the present while data science is looking towards the future and predicting what might happen next. BI works with historical data in order to determine a responsive course of action while data science creates predictive models that recognize future opportunities.
Data Types
Business intelligence works with structured data that is typically data warehoused or stored in data silos. Similarly, data science also works with structured data but predominantly is tasked with unstructured and semi-structured data, resulting in greater time dedicated towards cleaning and improving data quality.
Deliverables
Reports are the name of the game when it comes to business intelligence. Other deliverables for business intelligence include things like building dashboards and performing ad-hoc requests. Data science deliverables have the same end goal in mind but focus heavily on long-term and forward-looking projects. Projects will include building models in production rather than working from enterprise visualization tools. These projects also place a heavyweight on predicting future outcomes as opposed to BI’s focus on an organization’s current state.
Process
The distinction between the processes of each comes back to the perspective of time, similarly to how it influences the nature of deliverables. Business intelligence revolves around descriptive analytics, this is the first step of analysis and sets the stage for what has already happened. This is where non-technical business users can understand and interpret data through visualizations. For example, business managers can determine how many of item X was sold in July from promotional emails versus through direct website traffic. This then leads to additional digging and analysis regarding why some channels performed better than others.
Continuing with the previous example of item X, data science would take the exploratory approach. This means investigating the data through its attributes, hypothesis testing, and exploring common trends rather than answering business questions on performance first. Data scientists often start with a question or complex problem but this typically evolves upon exploration.
How Do BI & Data Science Drive Decisions?
While business intelligence and data science are both used to drive decisions, their perspective is central to determining the nature of decision-making. Due to the forward-looking nature of data science, it’s most often at the forefront of strategic planning and determining future courses of action. These decisions, though, are often preemptive rather than responsive. On the other hand, business intelligence aids decision-making based on previous performance or events that have occurred. Both disciplines fall under the umbrella of providing insights that will support business decisions, but the element of time is what distinguishes the two.
However, it’s important to note that this might not always be the case for every organization. The lines between the responsibilities of BI and data science teams are often blurred and vary from organization to organization.
Conclusion
Despite their differences, the end goal of business intelligence and data science is ultimately aligned. It’s important to note, though, the complementary perspectives of the two. Examining the past, present, and future through data remains vital to staying competitive and addressing key business problems.
Perspective
