Data Analytics: What Insights can you gain from Business Data Analytics

While juggling your business activities with meeting consumer demands and complying with regulations, you must have come across technological terms like insights, data, and information. While data is your raw information, insights are often the misunderstood concept. Many organizations actually fail to understand that insight is not a single data point nor an observation nor a customer wish or statement. Insight is in fact, a new approach to view the current market prompting us to re-evaluate existing methodologies and challenging the status quo.

Before we step down to different insights extract-able from raw data and information, let us first have a brief idea about data analytics, its types, examples, and challenges.

What is Data Analytics ?

As its name suggests, data analytics is analyzing raw data to find trends and patterns. It defines the various techniques and processes used to examine the available datasets and draw inferences from the obtained information. It constitutes a set of qualitative and quantitative approaches to extract valuable insights from data. Data analytics provides a way to differentiate important phenomena from the noise present in data.

According to Marshall and Rossman, data analytics is a fantastic and creative process to organize, structure, and render meaning to a mass of collected data irrespective of its ambiguity.

Types of Data Analytics

Data is the most valuable asset for every organization or individual. Data analysis is now an inseparable part of every business process, providing the much required understanding of previous events and decision making capabilities for future activities.

Enterprises around the globe are using different types of data analysis techniques based on available technology and business needs. Each of these types is interlinked beginning from the easiest one to the most sophisticated and complex type.

Infographic below shows different types of data analytics starting from the bottom level to top level of complexity.

Figure 1: Types of Data Analytics

Why is Data Analytics Important ?

Data is gushing into your organization from every source, customers, machines, internal departments, everywhere. However, this asset would be a menace if you actually do not have any plans or strategies to optimize it, contextualize it, and represent it as meaningful information. Data analytics is actually your pathway to effective data-driven decision-making capabilities.

Despite the growing trend of big data, companies are still in nascent stages of utilizing the full potential of big data. These 5 reasons mentioned below would surely be substantial enough to impel you towards adopting the most feasible data analysis method and technology for your business.’

Firstly, you can always leverage data analytics to monitor your product or service performance in the market, get information on customer needs and behaviours, determine areas of improvement, and accordingly alter your strategy to fulfil your potential customer’s demands.

Secondly, predictive data analytics using artificial intelligence and machine learning would always guide organizations towards new innovations in products and services, thus maintaining a sharp edge advantage over their peers in the market.

Thirdly, data analytics allows you to streamline your business processes in a seamless and effective manner. You can get accurate data to facilitate smoother business driven decision making.

Fourthly, you can always keep a tab on the financial expenditures of each department of your business using data analytics. By consistently monitoring the performance as well as expense data, you can have a clear picture on where to invest more and where to cut the cost in terms of operations, technology, and resources.

Fifthly, Data analytics would help you determine the most effective marketing strategy in getting potential target audience and retaining them. By tracking which methods of advertisements are able to attract maximum customers, you can devise plans to adopt the same.

What is Data Analytics with Examples ?

As mentioned above, data analytics is the art of examining raw data to draw conclusions about your product or service, thus devising strategies to improve your brand value, increasing sales, boost customer retention and acquisition, and ultimately increase your business profitability. With advent of multiple state of art business intelligence tools around the corner, data analysis has come a long way from simple spreadsheets to advanced visualizations.

While some businesses use data analytics to generate fraud management report, some use to track their departmental performance in real time. Some like Google use data analytics to assess user visit counts, location, and device of accessing the sites, etc.

Given below are two examples of data analytics incorporated in the healthcare and sales department.

Analytics Report for patient consultation in a healthcare institution.

Figure below shows a dashboard showing report on count of patients visiting a healthcare institution with respect to specialties, time, and referral type.

Figure 2: Healthcare Analytics Report

Analytics Report on sales performance of an e-commerce platform.

Figure shows a report on how an e-commerce platform is performing in current year by analyzing the sales with respect to products, AOV trends and finding top customers.

Figure 3: E-Commerce Sales Analytics Report

Special Considerations: Who is using data analytics?

Big data analytics has evolved a long way from capturing numbers on spreadsheets and manual analysis to using sophisticated software systems for accelerating and improving the analytical processes. Companies around the globe are transforming their operational and production strategies to move towards an effective solution where the important patterns, correlations, and trends are no longer latent.

Given below are top 3 special considerations on the best possible uses of big data analytics along with who is actually using data analytics.

Figure 4: Data Analytics Use Cases

Data Science vs. Big Data vs. Data Analytics

With advent of new technologies, the data landscape has expanded into new horizons. Buzzwords like big data, data analytics, data science can be heard everywhere in your surroundings. Despite their unique implications, people often tend to mistake one for another and consider them to be same.

This might not sound an issue when your ultimate target is to get a profit driven business model. But when your requirement is to recruit desired individuals, it is always recommended to have basic knowledge of skill sets and job roles for each profession.

Let us now learn about the basic difference between big data, data science, and data analytics

Big data is the humongous amount of data consisting of large and complex data sets. It encompasses of both structured and unstructured data inundating businesses on a daily basis.

Gartner says: “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Data Analytics is the art of examining raw data to infer insights and look for correlation between datasets. It involves inspecting, cleaning, modifying, and modelling data. Data analytics actually helps businesses gain solutions to complicated business situations. Job of a data analyst is to leverage available data to help businesses take profitable business decisions.

On the other hand, Data Science is an umbrella term encompassing every stage involved in a data driven business process. It involves use of different tools, algorithms, and technologies to find hidden pattens in raw data, develop new data models, and devise strategy to deploy these models.

Pat Gelsinger, CEO, VMware says “Data is the new science, big data holds the answers.”

Database vs. Data Warehouses vs. Data Lakes vs. Data Marts

IDC predicts the global data sphere will enlarge to 175 ZB by 2025. With new technologies and tools around you, gathering data is now an effortless process for organizations.

Given below statistics shed light on the growing trend of big data around the globe.

  • YouTube holds about 1 billion GB data on its servers, with every minute about 300 new hours of video being uploaded.
  • About 31 million messages and 2.7 million videos are sent across Facebook with users sharing about 100 TB of data daily.
  • About 5 QB of data is produced daily by smart devices such as sensors, fitness trackers, smart speakers, etc.
  • 80% of the data collected by organizations will comprise of unstructured and semi-structured data.
  • About 463 EB of data will be created every day by 2025.

Figure below gives a somewhat medium level comparison between these 4 methods of enterprise data storage.

Figure 5: Comparison of Database, Data Warehouse, Data Mart, and Data Lake

What Insights can you gain from Data analytics?

From the above discussions, it can be deduced that data, information, and insights are the three most important tools to optimize business processes. While data is the collected raw numbers and text, analytics is understanding and processing the information and insights is the valuable learning outcomes gained by analyzing the data.

Insights can be termed as the nuggets of gold obtained by divulging deep into the ocean of data. These days, insights can be augured as the key to innovation, basis of business development and marketing.

According to the management in General Mills: Insights are something you know that your competitors don’t. But I think insights go deeper than these statements would imply.”

Before you start extracting insights, it is important to recognize whether these findings are actually helpful for your business. Determine whether the insights reveal anything about your customer, captures your consumer sentiments, takes important stakeholders into account, or challenge your company for innovation.

Cristina Quinones from Pontifical Catholic University of Peru mentions about 9 types of insights important for your data driven business decisions: Consumer, Cultural, Product, Future, Product, Brand, Market, Purchase, Owner, and Usage insights.

Consumer Insights

A consumer insight defines deep truth about existing as well as prospective audience who are using a company’s product or service. ‘Truth’ here refers to human behavior in terms of their experience, belief, needs, or desires. Effectively conducting consumer insight research can enable companies to improve their customer experience, thus increasing sales and revenue.

Cultural Insights

Cultural insight is the practice of tracking, measuring, and highlighting the various factors driving the choice and perceptions of consumers. Marketeers can now capitalize on cultural insights to get solutions resonating with their target markets.

Future Insights

A future insight accords a unique suggestion or idea about the future through well-articulated evidence, logic, and reasoning. Compared to regular insights, future insights are more hypothetical in nature with the need to establish contexts which do not exist and future conditions to interpret experience from within.

Product Insights

Product Insights empowers organizations to deliver desirable product experience to customer by leveraging real time user feedback. Product insights are extracted by considering different factors such as type of products, methods and metrics of user feedbacks, and the timing aspects of product development and lifecycle.

Brand Insights

As its name suggests, brand insights provide businesses with a guide to improve their brand success. By inferring brand value and customer sentiment, companies can assess their relative market position, uncover new opportunities, retain, and acquire customers, and ultimately increase their revenue.

Market Insights

It is the discovery of current market trends, opportunities to generate growth. It defines unleashing the potential of previously unrealized target market which can benefit both the audience as well as the organization. It describes the activities performed to assess consumer behaviour, get intelligence on market management function, and reduce the risks in marketing decision making.

Purchase Insights

Purchase insights is using the distribution data, promotional activities, in-store experiences, and retail dynamics to increase business sales and marketing efforts. Marketing strategies are devised based on what the purchases buys and how and where he or she buys the products or services. It helps improving equity and profitability of a brand by ensuring shopper-oriented changes in business environment.

Usage Insights

As its name suggests, usage insights give you a clear picture about the usage pattern of your service or product. It helps you unravel unused metrics of a software, if the utilization is increasing or diminishing, or what versions or features are used.

Owner Insights

Owner insights give businesses a sneak peak into the current performance and working of their peers in the market. It allows you to discover benchmarks to consider for creating priorities and action plans. Business owners can leverage owner insights to gain competitive edge over their peers.

Challenges of Data Analytics

Recent years have seen a tremendous growth in global data and its applications spread across the industries. Even though advanced technologies like artificial intelligence, augmented analytics, machine learning are driving businesses towards a rapid and consistent level of elevation, the analytics industry is still prone to many challenges like lack of talent, inability to reach the right consumers, storing data, and many more.

According to Patrick Stokes of Salesforce, the toughest part of implementing data analytics is having a point of view on using the data, without which one cannot build a related bespoke technology.

Given below are some more challenges faced during big data analytics:

#1 Churning out meaningful insights from the ocean of data

While data has come a long way from being a mere observational asset to knowledgeable information, there is still a long way to go for extracting actionable insights from this information.

Abhinav Kimothi, Senior Manager, Analytics, Lymbyc Solutions says “While the challenges of the journey from observation to information have been solved almost completely, challenges remain in gaining actionable insights out of information. The separation of insight from the noise of information is the next big leap that analytics has to take.”

In fact, the biggest challenge faced by enterprises today is the lack of potential or capability to capitalize on the benefits of big data in effective achievement of business goals.

#2 Lack of synchronization among multitude of data sources

With the plethora of data sources around you, storing data whether structured or unstructured is not an issue. However, what is challenging is the need for integrating these different pieces of datasets, which can otherwise lead to incomplete and inaccurate analysis.

Faisal Hussan, Co-founder and CEO of Synechron says “Enterprises are dealing with an overwhelming amount of data coming in from multiple disjointed channels. Manually compiling this data becomes time-consuming and prone to human error, resulting in an inaccurate analysis. Lack of a centralised platform or system leads to difficulty in synchronising the data sets and deriving meaningful insights.”

Having said that, the biggest challenge is to incorporate data sets from disparate sources into a single analytical platform.

# 3 Diminishing data quality is having a cascading effect on the effectiveness of analytics

Availability of large amount of data sources does not only brings forth the issue of integrity, but also data quality, especially when you are dealing with unstructured data.

These 5 traits are essential for data to be qualified as rich in quality:

Unfortunately, these above-mentioned qualities are a challenge to sustain with a large number of errors encountered because of combining unstructured and inconsistent data from different sources.

#4 Vulnerability of data owing to high risk of data exposure

Despite the vast range of benefits and opportunities associated with big data, one cannot ignore the potential risks that come along with it. These risks are primarily related to data privacy and security.

Patrick Stokes, Executive Vice President, Product Management, Salesforce says “We really lean into this idea of trust and treating all of our customer’s data as if it’s sacred. What we’re moving into now is a world where we help our customers treat their customer data the same way and impose that trust down to them.”

Organizations need to comply with Government legislation such as GDPR, CCPA, etc. and implement stringent policies to handle customer privacy.

#5 Lack of trained professionals with data comprehension and handling skills.

As per Northeaster University report, a dearth of qualified data scientists and analysts with ample skills and knowledge to work with large amount of data is the biggest analytics challenge of 2020. Unfortunately, there is huge gap between the demand of analytic talent and the actual supply by the education institutions.

Manashi Kumar, Chief of Strategy and People Operations, BARC India, says “Also, due to huge demand and supply gap, the talent does not shy away from looking out for more lucrative opportunities, leading to high compensation-related attrition,”

While retaining talented individuals being a problem, enterprises are moving towards training programs to upskill internal manpower.

Figure below shows statistics summarizing the challenges faced by people from different organizations:

Figure 7: Challenges of Data Analytics

How to Turn Data into Actionable Insights ?

1. Define a strategy

It is always important to have a clear picture of your business goals before embarking on the journey to extract insights. There is no point in implementing your data projects with defective strategies or expired insights. Make sure to meticulously research each and every area of your business such that you can always narrow your strategy to core problems. Remember to always formulate the right kind of questions which can not only utilize your stakeholders’ aspirations and challenges but also enhance economic opportunities.

2. Identify Customer Requirements

Customer is always at the kernel of any business sector with customer satisfaction being the primary goal of companies. Extracted insights would be meaningless unless you have ample knowledge about your customer’s preferences and needs. The blueprint is to interact with consumers of your product or service, collect their feedback, and finally leverage the qualitative data to improve your brand.

3. Hire apt and smart manual resources

Technology would be nugatory without any assistance of human intelligence. Remember, tools can only give present you data, it is the creative minds which can formulate new ideas and strategies, and conscientiously implement them. Therefore, it is always important to hire smart individuals with apt knowledge in field of machine learning, AI, big data, and many other latest technologies. Extracting insights and converting them into actions is a teamwork and requires equal efforts from both the internal experts as well as external analysts.

4. Integrate the data sources

A perfect dataset from a single source is actually a myth and hence, reliance on a myriad of sources is inevitable. However, data from isolated sources is worthless unless you have plans to integrate the data sources. An integrated platform would provide you with quicker insights and help you enlarge your business horizon. Instead of looking for data in the entire warehouse, it is always advisable to analyze data from the small data mart.

5. Adopt the right visualization technique

Data visualization is the most imperative stage of data-driven business and is the main way to convert your data into valuable insights. Therefore, it is necessary to have visualizations that are cognitive, predictive, and prescriptive. Visualization is not just about graphs, charts, and fancy images. It is, rather, an important mode to identify trends and help us differentiate between useful and germane patterns.

6. Discover data context

Your countless hours and efforts in diagnosing a business problem would go in vain unless you are able to recognize the context behind the situation. Discovering context would help you identify interrelated conditions leading to the current situation and put things into a perspective. Remember, context actually provides facts which can be converted to actionable information, and eventually to successful business decisions.

7. Develop a healthy organization

Organization silos is one the major setback in any organization thriving on data for its success. A healthy organization is possible only when you promote communication instead of confrontation, collaboration instead of isolation, developing a motivating and positive environment. In fact, the insights you have extracted might be more valuable when you are successful in assimilating feedback from every individual involved in all stages of data driven business.

8. Articulate the Insights carefully

Before recommending actions for the extracted insights, it is always advisable to look out for missing values or blanks in the data sets. Take a step back, have a re-look at the insights, and proceed only when you feel there is no need for any change. You must apply ‘common sense’ as to whether these insights are actually worth, before converting them into actions.

Data Analytics Trends

Thanks to the amalgamation of data with advanced technologies like artificial intelligence, machine learning, and virtual reality, businesses are now moving from being reactive to proactive in meeting client demands, thus ensuring their progression amidst this global crisis. Unfortunately, 2020 has been a severe pandemic year, and data analytics is playing the most important role in foreseeing spread of disease, looking for new cures, and medical management of the pandemic.

Rita Sallian, Distinguished VP Analyst, Gartner, says: To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,”

These trends mentioned below would help organizations sustain through this global crisis situation and prepare themselves for a post-pandemic restart.

Figure 8: Top 6 Data Analytics Trends of 2020

Conclusion

The innovation business has really changed the way we are perceiving data and looking for insights. Data, analytics, and insights are the three strong pillars for a business to successfully thrive in this ever-competing world. Recent years have seen a massive rise in tools that provide the much-required platform to collect data from disparate sources, perform analysis, and gain valuable insights through advanced technologies like smart analytics. BIRD is the emerging full-stack data management tool allowing businesses to gain valuable insights into their business and improve their marketing strategies.

Explore BIRD’s smart insights storyboards by clicking here.