In this digital universe, data is undoubtedly ubiquitous and of course overpowering. Current trends of business growth depend on the effective implementation of a business intelligence suite. This in turn depends on leveraging the avalanche of data available, thus encouraging the management to gain insights on the data, perceive on-going processes and strategize ways to improvise current trends.
According to IDC’s Data Age 2025 report, data driven world will always be tracking, monitoring, watching, listening ———–forever learning.
Gartner analyst and VP Rita Salam says, “Data and analytics leaders should actively monitor, experiment with or deploy emerging technologies.”
This statement emphasizes how data and analytics play a pivotal role in various business activities like serving customers, recruiting people, optimizing finance, supply chains, and many other functions. Big data analytics and business intelligence are undoubtedly revolutionizing the way companies are orchestrating their day to day business activities and rearchitecting themselves to remain agile and competitive.
However, before capitalizing on analytics to drive data utilization, it is necessary to count on a strong data governance system. This can enhance the data comprehensibility of a business user, and in turn, provide the much-required boost to gain more intelligence and insights.
Through this article, we aim to apprise our readers with the importance of having an effective data governance strategy before proceeding to share some worthwhile data analytics methods and approaches.
But, before that, we will get to know why we need data analytics in the first place, and is it the same as data science or not.
How are Data Science and Data Analytics Important?
According to Forbes, the total data market is expected to increase to a whopping amount of about 132.3 billion US dollars in this current year.
According to IDC’s data report, the reliance of the economy on data is increasing manifold with many companies capturing, cataloging, and leveraging data in every stage of the business process. The figure below demonstrates the never-ending expansion of the global data sphere as reported by IDC.
This statistic is self-explanatory enough to infer the credibility of data usage in businesses. However, unless and until proper and authentic ways to comprehend, munge and process these large data sets, are defined, hours and days to glean this data or information would be undermined.
And this is where data analytics and data science come into the picture. The terms data analytics, big data, data science, and other technical jargon seem to be correlated with each other and are often used in lieu of one another. However, they have different implications in business scenarios and utilize their own unique approaches.
It is essential to leverage the vast chunk of data bought to an organization’s table and optimize big data analytics. Therefore, it is imperative for an individual or business user to understand the nuances of basic differences of data science and data analytics.
Top 4 Differences between Data Science and Data Analytics
Defining Data Science and Data Analytics
As its name suggests, data science is a multidisciplinary field including numerous methods and specialties to extract valuable information from a given data and perform predictive analysis to unearth valuable questions and their related possible answers.
On the contrary, data analysis is a more narrowed down and focused version of data science. The concerned person or data analyst parses through an organized data to obtain nuggets of information critical to provide solutions for current problems.
In simple terms, while data science provides solutions to problems we do not know even existed, focus is more on the ‘questions’; data analysis aims to provide lucrative solutions to known problems.
What does a data scientist and data analytics work on?
A data scientist performs the following tasks:
- Delves deeper into the gleaned data through various exploration techniques
- Finds unique patterns and insights into the data
- Formulates queries whose resolutions are most likely to benefit the business.
On the other hand, a data analyst performs the following tasks:
- Applies modern perspective to comb through obtained historical data.
- Optimally uses his or her expertise in finding operational insights into complicated business arenas.
- Applies various techniques and methods to find possible solutions to existing problems.
What are the roles and responsibilities of a data scientist and data analytics?
A data scientist needs to fulfil the following roles and responsibilities:
- Exploratory data analysis
- Process and prepare the data
- Generate insights
- Identify trends
- Make predictions on the data.
On the other hand, a data analyst has the following roles and responsibilities:
- Gather the data
- Prepare and clean the data.
- Use statistical tools to analyze the data
- Develop a visualized version of the data in form of graphs or charts.
Skills required for data analyst and data scientist
Fourthly, A data analyst is expected to be a master in programming languages such as SQL, Python, R, HTML, have good mathematical and statistical skills along with sound knowledge of data visualization tool.
On the contrary, a good data scientist is expected to possess all these above-mentioned skills along with a strong ability and acumen to transfer the findings into strong business opportunities. He or she must also be well versed in the field of machine learning and artificial intelligence.
Figure below shows some intriguing facts and figures demonstrating difference between data science and data analytics.
Top 5 Examples with Real Time Scenarios
Let us have a quick glance at a few real time examples of data science by different enterprises.
1. Cancer care recommendations by Oncora Medical:
Data scientists, from Oncora Medical at Philadelphia, joined forces with the radiologists to gather cancer related data from about 50000 records.
The data included diagnosis, treatment, and possible side effects.
Based on this data, personalized recommendations like chemotherapy and radiation regimes were created for each cancer patient using a machine learning algorithm.
2. Optimized packaging delivery by UPS
UPS, the multi-billion global logistics company utilizes its Network Planning Tools (NPT) software to incorporate data science techniques such as machine learning and artificial intelligence to process its logistics tasks.
These techniques help develop ingenious ways to deliver packages safely and on time, in extreme conditions like bad weather or other bottlenecks.
3. Giving a new face to marketing by Amazon
The data science team at Amazon often keeps a track on user actions such as the categories or products browsed frequently, spontaneity in choosing and buying a product, etc.
They utilize this data to develop a recommendation model for providing product suggestions pertaining to each user. This explains how the favorite Reebok shoes you had just browsed on Amazon, pops up on the prime video screen.
Let us now shift our attention to actual examples of data analytics.
4. Improving employee coordination and productivity by Microsoft
Back in 2015, Microsoft devised a strategy to curb employee traveling time by moving most staff in one building. The workplace analytics team concluded upon a hypothesis of improved collaboration by moving a 1200 team from five buildings to just 4 buildings.
Data analysis techniques like appropriate statistical tests were done to check the actual outcome of this hypothesis and the resultant implemented plan succeeded in saving about 100 hours per week and an estimated saving of about 520k US dollars per year.
5. Anticipating Machine Failure by Shell
The oil giant Shell utilized a software based analytical platform to harvest data related to its various drilling machines’ parts and anticipate the possibility of any failure.
It would utilize tools like Databricks to capture streaming data and strategize purchasing the machine parts, storing, and placing the inventory items.
The area of data science is an umbrella encompassing data mining, data analytics, machine learning and other familiar subjects. On the other hand, data analytics is a more specific and concentrated part within that umbrella.
Both data science and data analytics have very minute yet imperative differences such as the skills required and application involved. However, despite the basic nuances, it is mandatory for any organization to meticulously select the right person for the right task.
As said earlier, before starting to analyze the vast chunk of enterprise data, it is essential to have a disciplined approach in leveraging the data values and managing risks. Merely storing data for compliance is just a burden on your finances. Rather, you need to deploy a data governance strategy to control data standards and ensure availability, usability, integrity, and security of business data. Below given section highlights on the needs of having a data governance framework in your organization.
5 Reasons Why You Need Data Governance ASAP
Data governance is defined by a set of processes, roles, policies, standards, and metrics in managing and protecting critical data. It is a four-way framework which ensures data availability, applicability, security, and integrity. It is a way to ensure consistent and secure data is used across the organization with common processes and responsibilities.
To be more precise, find the figure below showing key elements of data governance:
Why is Data Governance Needed?
“With bad data, we keep making bad decisions. We just don’t realize they’re bad decisions until later.”
This is a quote by Scott Taylor from MetaMeta Consulting. His saying is enough to provoke us towards the vitality of data governance in getting cleaner and leaner data, which in turn results in better analytics, then better business decisions, and eventually leads to better business results.
Given below are few excerpts from a recent Gartner report on top prospective data and analytics trends in future
- By 2024, 75% of companies will deploy Artificial Intelligence as mainstream technology rather than merely being a start up in this aspect.
- By 2023, decision intelligence would be primary focus of analysis for about 33% of organizations.
- X analytics would be the new emerging term with X defining data variable for a range of structured and unstructured data.
- By 2022, 90% of data and analytics innovation would be driven by public cloud services.
While data governance has always kept enterprises on their toes, above points highlighting growth of advanced technologies like AI, MI, DI, etc. only add fuel to the fire.
Donna Burbank, MD at Global Data Strategy says: “Organizations are realizing that AI is only successful when built upon a solid data foundation, thus driving the need for Data Governance.”
This implies for an urgency to gain much required experience in implementing effective governance strategies with actual and appreciable results.
“If the data is not reliable or of poor quality, less than-optimal business decisions are likely”. Says Bill Tomazin, Managing Partner, West Region and National Audit Solutions, KPMG LLP US.
Still not convinced of the worth, consider below given 5 reasons:
1. Data Governance Provides Data Consistency
A business cannot thrive with separate and opposing views of different stakeholders. It is important for persons at each level of an enterprise to be at the same page about a particular subject.
For this, the users should have access to reliable and common data across different departments. Also, effective data governance strategy is valuable to ensure siloed departmental information resources are harmonized to ensure good quality data and lesser clashes at executive levels. This would in turn accelerate business decisions and ensure business success.
2. Data Governance Ensures Data Availability
Business intelligence systems deployed by organizations are of no use if data is limited to individual departments and without being accessed by deserving users.
Data governance ensures required data is available to each department of an enterprise and stored data is organized using schemas.
It also ensures trustworthy tools are available to capture and process critical data. Data governance provides a 360-degree view of important data for business purpose.
3. Data Governance Ensures Regular Compliance
Importance of regulatory compliance cannot be ruled out in today’s data driven business world, especially in this time where protecting private and personal data is need of the hour.
Consider GDPR (General Data Protection Regulation) established to protect European Union residents’ personal information. It authorizes the individual to request his data to be deleted from business databases. Lack of a proper data governance strategy and failure to meet this request would result in draconian fines for companies.
Data governance is therefore needed to define how data is captured, stored, and secured against misuse, theft, and accidents. It also includes effective audit and control strategies to ensure data is used with proper and authentic procedures.
4. Data Governance Improves Quality of Data
Effective data governance ensures clean, standardized, and accurate data is available at your fingertips; effect of which would echo throughout the organization. However, this cannot be accomplished if you are knowingly or unknowingly working with stale or irrelevant data.
As per a recent survey, organizations across the globe store about 33 percent of redundant, outdated, and unimportant data.
Hence, a good data governance strategy is important to separate useless data from useful data and destroy the former. It should also prevent unnecessary duplication of data and ensure dirty and unstructured data does not clog your database.
5. Data Governance Saves Expenses
Your marketing, sales, finance, and analytical efforts would be wasted if majority of your time is spent in identifying and tracking down duplicate data.
Data governance ensures your data is clean and standardized. This allows you to utilize your precious time to leverage data for successful business. With implementation of standard rules defining and governing your core data, you are sure to gain operational efficiency over time. And time saved is money saved.
Until and unless effective data governance program is not implemented, massive troves of big data would only result in data swamp rather than expected profits from gained insights and intelligence.
Top Methods of Data Analysis
Despite the availability of disparate tools and techniques in business intelligence, effective analysing and comprehending the collected data suitable to drive business requirements is always a challenge. Key to overcome this situation lies in finding an effective analysis solution to effectively curate, organize and apprehend the plethora of potential boosting information.
This article lists out vital techniques for enthusiasts to reacquaint themselves with state-of-the-art data analysis techniques for an effective transition from abstract figures and text to quantifiable information with context.
Analyzing Quantitative Data
Quantitative data is associated with numerical findings where data is computed using statistical methods and is quite objective in nature. It is often selected in samples and then analysed. Research patterns obtained from the samples can then be applied to a generalized population. Hence, analysing quantitative data needs a lot of expertise in accurate aggregations and data interpretation.
Quantitative data analysis is itself segregated into two types – Descriptive statistics and Inferential statistics.
The most basic form of numerical data analysis and as the name suggests, descriptive analysis provides a more elaborate description of data. It is the core of entire data analysis, with more emphasis on what transpired.
Examples include sales leads, key performance indicators and monthly revenue reports.
Focus is basically on summarizing past behaviours and applying the analysis to depict upcoming performance.
Data can be described either in terms of measure of centre (central tendencies) or as a measure of spread, as mentioned below:
One of the most quintessential concepts in statistical analysis, the central tendencies depict the behavior of the dataset in terms of a central notion such as mean, median, or mode. The central tendency can be described using the following terminology:
Mean: Represents the average data among the collected sample and is obtained by dividing the sum of values by number of values.
Median: Represents the central value in the data set, arranged in ascending order. Median for an even data set is obtained by calculating the average of two central values.
Mode: Represents the recurrent value in the data set.
Figure below shows a BIRD visualization demonstrating sales for different closing dates with all types of central tendencies as well as measure of spread.
Measure of Spread
Widely used in conjunction with the central tendencies, the measure of spread depicts the dispersion of random variables within the data set.
Range: The range represents the difference between high and lower threshold of the data set. It is typically used to detect errors in the data set.
Variance: Variance depicts the possible deviation of actual value from the expected value for a specific behaviour. It is computed by squares of deviations of each value from the mean.
Standard Deviation: Standard deviation measures the spread of given values from the mean. It is calculated as the square root of variance.
As advocated by its name, the inferential method relies on statistical methods to make inferences from the given data set. It allows the analyst to make predictions by analysing the given sample. It is generally used to study whether an observed data pattern is accidental or intentional due to any intervention effects.
While there are multiple methods in inferential statistics, given below are three most widely used inferential analysis techniques for the reader to upgrade his or her analytical skills:
A hypothesis testing is essential in making relevant decisions using sample data. The procedure evaluates two contradictory statements about a generalized population to infer the best statement supported by the sample.
It basically involves implementing statistical tests to compare one statement (accepted fact or null hypothesis) with another statement (alternate hypothesis). Mathematically, deviation of a calculated test statistic from its already mentioned critical value determines whether the null hypothesis should be rejected or accepted.
Types of hypothesis testing include T-testing, Z-testing, Chi-Square testing, etc.
This modeling technique is best suitable for predictive analysis, to forecast future trends or possible impacts of any decision. Regression exploits the possibility of a relationship between a dependent variable (target) and one or multiple independent variables (predictors) and helps estimate the value of that dependent variable.
It is significant in predicting relation between two variables and impact of various independent variables on that single dependent variable. For example, a company’s sales growth can be estimated by having a knowledge of its current economic conditions.
Types of regression analysis include linear regression, logistics regression, polynomial regression, etc.
Figure below shows an example of linear regression wherein the relation between original price of each item and base price of each item is demonstrating using a linear relation.
Similar to regression, correlation also utilizes the concept of possible relation between two quantitative variables. While regression depicts impact of multiple independent variables on a dependent variable, the correlation determines strength of relation between two or more variables.
Mathematically, relation between two or more variables is defined in terms of correlation coefficient on a scale of +1 to -1. For example, identifying a correlation between behavior of consumers and a type of product or service can help marketers and salespersons to leverage this information in boosting up business opportunities.
Figure below shows an example of correlation demonstrating strength of relationship between different variables pertaining to the sales within a city. As seen, correlation between sales and profit is maximum with a correlation of 0.826.
Analyzing Qualitative Data
Analyzing qualitative data requires a more subjective approach as here the analyst deals with words, symbols, images, etc. rather than numbers. It involves parsing a major chunk of transcripts to identify possible similarities and differences, and eventually theme or categorize the given information.
Analyzing qualitative data requires a lot of homework as given below:
Mentioned below are few basic methods used in qualitative data analysis:
The grounded theory process involves building up a systematic theory based on observations. Researchers study a variety of cases in different environments to conclude upon a final explanation. The researcher refines problem statements or concepts to recheck the phenomenon until an appropriate explanation is reached upon.
Brene Brown, leading grounded theory researcher says, “maybe stories are just data with a soul.”
The content analysis technique involves categorizing given subjective data into different similar themed segments using color codes or other means. The researcher can then identify, process, and analyse certain words, themes, or concepts to make inference about the message, people, or environment at the time of that text. This is used mostly to analyse data from open end surveys, field research notes, conversations, etc.
The narrative analysis focuses on elaborate content or narration in the form of stories or experiences shared by the audience to generate desired solutions to current problems. The information comes from sources like interviews, field observations and surveys.
Discourse analysis method in qualitative research concentrates on finding a conceptualized meaning to a given data rather than merely on its language use. Data is analyzed thoroughly to interpret the tone of language used in the text and the possible reasons behind any change. Sources of information include books, periodicals, newspapers, brochures, business documents, websites, forums, etc.
Even though there are a variety of techniques for data analysis, none can be termed as a golden standard or a right way for realizing effective results. Any method used clearly depends upon the type of collected data and possible interpretations from that data.
Exploratory and Confirmatory Approach to your Business Data Analysis
Objective of exploratory data analysis is to generate new ideas by examining large datasets and finding patterns within the observed information. An exploratory data analysis creates a first-hand hypothetical model.
Confirmatory data analysis involves testing validity of the already defined hypothesis and using appropriate techniques to confirm an existing theory or create a new theory.
Both exploratory and confirmatory analysis methods are complementary components of a company’s objective to discover relevant findings and leverage these findings to the business’s success.
This section describes the basic nuances between exploratory and confirmatory data analysis approaches while focusing on their interrelation as well.
How are Exploratory Data Analysis and Confirmatory Data Analysis Related to Each Other?
Even though differences between exploratory data analysis and confirmatory data analysis are inevitable, both these approaches are intertwined with each other to create the best possible analytical model.
While exploratory data analysis sets the stage, confirmatory data analysis performs the act. Both these approaches are paramount in generating an optimal result. Regression analysis is no importance unless a hypothesis is developed.
Let us consider below given scenario:
An online retail shop has recently seen an increase in number of subscribed users for the past few months. This is the phenomenon, and our objective is to find the underlying cause and ways to promote this trend.
Initial stage is exploratory data analysis wherein sales data for each category is collected, cleaned, and plotted to visualize percentage of customers for each product category in the past few months. The graphical model identifies the trend of maximum number of users purchasing sports shoes. On further investigation, it is revealed that last two months had been a holiday season with most of the schools and colleges closed. Now, the hypothesis is sport shoes are bought for teenagers during holidays.
Once hypothesis is set, next stage is evaluating the evidences to challenge your assumption. An inferential statistical model is built to analyze sale of sports shoes based on demographic category and for the same months during last few years. This is confirmatory data analysis.
Differences between Exploratory and Confirmatory Data Analysis?
Infographic below explains top 5 differences between exploratory data analysis and confirmatory data analysis.
In this highly competitive and fast-moving world, your enterprise data is the key asset to determine your business success. Each and every organization is impelled to enforce digital transformation as its core strategy. Your company should be well equipped with the right tools and right collaboration among stakeholders with clear goals, well-defined processes, and regulations to ensure a smooth data governance strategy. In fact, that is the most subtle way to ensure your business success through data-driven business decisions.
Ready to incorporate a full stack solution for your overarching enterprise data needs? Well, you can always count on BIRD for agile and astute business decisions.