Data Science vs. Business Intelligence
Data is the new – oil, currency, bacon, gold, water, soil. These are all options Google autocomplete listed as predicted search phrases. It’s clear that data is highly valued, and rightfully so. Organizations are using data to do amazing things; the key term being “using,” rather than just collecting.
The process of gathering data is fairly well established. In fact, forward-thinking organizations started collecting data even before they knew how they were going to use it. They recognized that data had great value, even if they did not yet know how to extract that value. The challenge is now how to use that data to gain valuable insights for the business.
Efforts among business intelligence and data science professionals are now focused on how to leverage all of the data. The volume, velocity, and variety of data are increasing in complexity. New data sources, structured and unstructured, in the cloud, and from SaaS applications need to integrate with legacy on-premises data warehouses. The need to make real-time decisions demands faster intake and processing.
Business intelligence and data science need to work hand-in-hand to address these challenges. To use business intelligence and data science effectively, you need tools that can handle both, seamlessly working with the same data.
What is business intelligence?
Business Intelligence (BI) is a means of performing descriptive analysis of data using technology and skills to make informed business decisions. The set of tools used for BI collects, governs, and transforms data. It facilitates decision-making by enabling the sharing of data between internal and external stakeholders. The goal of BI is to derive actionable intelligence from data. Some of the actions that BI may enable are:
- Gaining a better understanding of the market
- Uncovering new revenue opportunities
- Improving business processes
- Staying ahead of competitors
The most impactful enabler of BI in recent years has been cloud computing. The cloud has made it possible to process more data, from more sources, more efficiently than was ever possible before cloud technologies came into use.
What is data science?
Data science is an interdisciplinary study of using data to extract meaningful forward-looking insights. It employs statistics, math, computer science, and subject matter expertise of whatever it is you are analyzing. The goal of data science is most often to answer questions that ask “what would happen if ….?”.
As with most sciences, technology and tools are an essential part of data science. Machine learning and artificial intelligence all play a big role. Cloud computing technology provides the agility, elasticity, and processing power required for data science solutions.
Data science vs. business intelligence
It is helpful to understand the differences between data science and business intelligence. It is equally helpful to understand how they work hand in hand. It is not a matter of choosing one or the other. It comes down to selecting the right solution to get the insights you are looking for. Most often, that means using both data science and BI.
Perhaps the easiest way to differentiate is to think of data science in terms of the future and BI in terms of the past and present. Data science deals with predictive analysis and prescriptive analysis, while BI deals with descriptive analysis. Other factors that differentiate are scope, data integration, and skill set.
Type of analysis
Data science looks at the probability of future events and conditions. Predictive analysis uses historical data to forecast business trends, customer behavior, and product success. It seeks to answer questions about what will happen in the future. Prescriptive analysis seeks to find a solution to a specific business problem.
Business intelligence looks at what has happened. It uses descriptive analysis to present historical data to business units in a way that makes it easy for them to visualize and understand. BI is often used to generate reports that clearly and accurately communicate the current state of the business.
Given that data science aims to predict events or conditions, the process starts with a specific idea or hypothesis. It then sets out to determine if the hypothesis is true. It is, after all, a science. Predictive analysis is performed on that specific hypothesis.
Conversely, business intelligence needs to be general in scope. The descriptive analysis must allow for any business unit to generate whatever type of report they need. For example, the data must support a product manager evaluating the success of his latest project or a sales director reviewing her quarterly results.
The data integration process of extract, transform, load (ETL) works well for business intelligence. It transforms the data before loading it into the data warehouse. This means that the data warehouse schema is known, which makes it easy for business users to use analysis tools to generate reports.
The other option, loading data into the data warehouse before transformation, is extract, load, transform (ELT). With this data integration method, data can be transformed at the time of the query. Because the query can be tailored to meet the needs of the specific analysis without being locked into a specific schema, ELT is well-suited for data science applications.
Data science is obviously the domain of data scientists. However, data science is not done in a vacuum. While data scientists need to have a well-rounded set of skills, they still need the help of the IT, operations, business units, finance, and others.
Business analysts are associated with business intelligence, and they certainly have the primary skill set for it. However, it is the business users that benefit most and have the greatest need for business intelligence. For that reason, most business intelligence tools have effective self-service capabilities. Without this feature, business insights would not be readily available to business users.
Finding the similarities between data science and business intelligence
Even with the differences between data science and business intelligence, there is a glaring similarity: they both use data to provide meaningful, actionable insight for an organization. Other similarities include:
- Garbage in / garbage out: The quality of data going into the system has a direct impact on how meaningful the results are
- Collaboration is essential: Neither data science or business intelligence works in a vacuum or in a culture of silos
- Better together: They both provide more useful insight when used together
- Cloud is the great enabler: While it may be possible to get use out of data science and business intelligence using on-premises technology, using the cloud will typically make it easier, faster, more agile, less expensive, and provide better insight.
- Easier: Provisioning servers and storage on a public cloud service doesn’t have the headaches associated with ordering and installing hardware.
- Faster: A new server can be up and running in minutes in the cloud, as opposed to weeks (or longer) to get an on-premise server and storage up and running.
- More agile: As you need to scale up or down with long term growth or for short term projects, cloud resources are agile enough to match your needs at any given time.
- Less expensive: While this depends on specific circumstances, provisioning cloud resources often ends up costing less than buying on-premises hardware. It also avoids hardware upgrade and refresh costs.
- Better insight: Collaboration is key to gaining better insight. Putting data tools in the cloud gives geographically dispersed teams better access to common data warehouses and analytics tools.
- Proper tools for a proper job: A set of tools that work seamlessly together and provide capabilities to ensure proper data quality, data integration, and overall data management is needed.
How data science and business intelligence work together
Although organizations can gain meaningful insight from either data science or business intelligence, using the two together provides the greatest insight to drive strategic decisions. Consider a situation in which a professional services company has been struggling to win proposals. They have limited resources to respond to RFPs, so they decide to use a data-driven process to decide which RFPs they are most likely to win.
The company chooses to use business intelligence to look at past RFP results and create profiles of customers and projects that have a high win rate. Then, using that insight, the company can create various hypotheses and scenarios, and use data science with machine learning to predict the likelihood of winning future projects. So, by using business intelligence and data science together, the company now has a profile of data on customers and projects that are in their sweet spot for winning business.
It’s easy to see how BI and data science each contribute to helping gain insight, but the combination of the two is what brings the greatest benefit.
The cloud and the future of “data science vs. BI”
Data scientists (perhaps under a different name like statisticians) and business intelligence analysts have been crunching data since long before cloud computing existed. In just the past few years, the quantity of data has exploded to the point where a single computer may not have the storage and processing power needed to handle so much data. This has made it necessary to move the storage and processing to the cloud.
Cloud technology offers several advantages:
- Inexpensive storage
- Fast processing
- Scalability to meet demand
- Easy data ingestion capabilities from many different sources, regardless of location
- Easy enterprise accessibility
- Intuitive self-serve tools which facilitates data democratization Dls
In recent years, much of the effort toward data management in the cloud has revolved around technology that connects to data sources and collects data. Organizations know two things:
- We can collect massive amounts of data
- This data can lead to better business outcomes
But not every organization knows how to get from step 1 to step 2. Organizations need a way to move from connection & collection to analysis & action. So the future of data science and BI in the cloud must focus on action rather than technology. This means that better infrastructure is needed to leverage the potential power of machine learning and artificial intelligence. That will, in turn, support better analysis of data.
Cloud providers have been working on this transition. Microsoft launched Project Brainwave, a hardware architecture designed to accelerate real-time AI calculations. The Project Brainwave architecture is deployed on field programmable gate arrays (FPGA) to make real-time AI calculations at a competitive cost and with the industry’s lowest latency. It runs on Microsoft’s Azure cloud computing platform. Amazon Web Services also offers FPGA in their EC2 F1 instances. This aligns with the industry-wide Infrastructure 3.0 that seeks to move us from connection & collection to analysis & action.
Getting started with data science and better BI
It is apparent at this point that data science and business intelligence have and will continue to have a very interesting relationship. They have the same general goal of providing meaningful data-driven insight, but data science looks forward while business intelligence looks back. That is not to say that one is better than the other. Each has a place that will solve different problems.
Despite their differences, there is a symbiosis between data science and business intelligence that can generate insights greater than the sum of its parts. With advances in cloud computing, machine learning, and artificial intelligence, that sum will grow in the future. For today, you need tools that can provide the benefits of this symbiotic relationship.
Three key features needed for data science and BI
In order to leverage the benefits of data science and business intelligence, you need a tool that, at a minimum, provides exceptional data quality, data integration, and self-service capabilities delivered in a single unified SaaS solution. These features need to be effective whether the data is on-premises, in the cloud (single or multi-cloud), or in a hybrid architecture.
- Data quality is critical. Data must be accurate, complete, and up-to-date to ensure that decisions made based on that data are valid. A solution’s data quality capabilities must include data profiling, cleansing, and enrichment.
- Data integration capabilities that can handle a growing number of data sources and increasing amounts of data. You need a solution that is capable of unifying the data from all sources to allow for complete and accurate analysis. A SaaS solution can enable this, and provide the elastic scalability, centralization, and cost benefits you expect from a cloud service.
- Self-service access to data is a growing demand from non-technical users. Data solutions must provide self-service access that is easy to use, regardless of technical skills, allowing users to explore, visualize, and analyze data. It will also offload some of the work that the technical data team would have to do to support those users, had they not been using self-service tools.
A single suite of apps built for BI and data science
Talend Data Fabric is a SaaS suite of apps that has all of the capabilities you need to gain the most insight from your data. Talend Data Fabric’s data quality capabilities provide clean, reliable data through intelligent de-duplication, validation, and standardization methods.
Data integration is enabled with the help of more than 900 connectors and components that simplify connectivity to all your data. Through a suite of self-service apps, business users can quickly access and clean the data they need to make on-demand decisions. Try Talend Data Fabric today.