In the real world, running a business requires knowledge, insight, skill, and creativity. Entrepreneurs use lessons from business school to put together a workforce, budget money, and develop a great product.
If all it took were determination and perseverance, most startups across the country would be successful. Unfortunately, even the most skillful and creative organizations can file bankruptcy in today's global economy.
The question becomes how can a startup improve its chances? One of the secrets to success relies on harnessing big data and data visualization to generate profit and encourage growth.
Properly collecting and managing data to learn more about customers and cash flow can be the key that separates the struggling businesses from the successful ones.
Nevertheless, many organizations aren't sure where to start or how to make sense of the data they do have. Gaining this understanding in a data-driven world can help to save money, increase revenue, attract new customers, and improve operational efficiency.
Read ahead to understand how data collection and statistical analysis contribute to growth and innovation.
The Power of Big Data and BA:
By 2022, 90% of companies will mention analytics as a primary asset to their business strategies
Data-driven organizations are 23 time more likely to acquire customers
83% of businesses pursue big data projects to gain a competitive edge
Data-driven marketers are 9 times more likely to achieve profitability
What Are Business Analytics?
Business analytics is the procedure by which a company utilizes statistical techniques and business intelligence systems to analyze historical information to make future decisions.
Its purpose is to allow users to gain a comprehensive understanding of inefficiencies and strengths and then use those insights to improve decision-making across the organization.
Analytics employs specific methodologies such as data mining and predictive analysis to convert large quantities of collected data into valuable information that pinpoints trends, outcomes, and potential disruptions.
A dashboard is the primary business intelligence solution utilized by a business analyst to collect, store, and track information. A set of KPIs is employed on the dashboard to allow everyone in the company to see what is needed to meet key objectives.
Due to the large quantities of internal and external information available, most businesses have employed some form of analysis to learn about their customers, assess the market, and analyze cash flow.
The best decisions are made by using data to assess historical patterns, trends, and past mistakes in these areas. Analytics improves trust in the decision-making process because choices are based on hard evidence rather than guesswork and instinct.
How Are Business Analytics Used?
Business analytics can be used for several purposes, depending on the size and scope of the company. Its primary use cases across different organizations include-
Analyzes information from different data sources- Sources can include anything from cloud-based applications, customer relationship management software (CRM), or social media marketing tools.
Finds patterns in data sets- Provides a set of statistics to denote historical patterns and gain insight into customer behavior. This is used to improve decision-making that affects future outcomes.
Monitor real-time KPIs- Everyone in the organization has visibility into current performance and what is required to achieve key objectives.
Confirms decision-making- Decisions can be made based on the most current and accurate information.
To perform business analysis, users partake in one or more of four methods. Each part of the four works in conjunction with the other parts to thoroughly analyze data and develop insights. These four types include-
1. Descriptive Analytics Descriptive analytics is the evaluation of historical information and key performance indicators to pinpoint patterns. The analyst employs a combination of data mining and aggregation to gain a comprehensive understanding of what occurred in the past and what is occurring in real-time.
It is most frequently utilized to drill down into customer behavior patterns to improve current and future marketing strategies.
2. Diagnostic Analytics This form of computer science analytics concentrates on historical performance to pinpoint which factors affect particular patterns. It is performed by drilling down into data to look for the element that triggered a particular event.
Once the analyst has discovered how likely the event is to reoccur, he/she programs an algorithm with programming languages for classification purposes (predicting a label) and regression purposes (predicting a quantity).
3. Predictive Analytics Predictive analytics uses data to predict future results by employing machine learning strategies. Analysts typically have to use insights derived from descriptive analysis to generate models that ascertain the probability of particular outcomes.
Sales and marketing departments often implement predictive analytics to curate a new campaign geared towards a segment of customers. Financially, predictive analytics is helpful to determine the future cash flow of the organization.
4. Prescriptive Analytics The prescriptive analysis takes historical performance data to gain insight into how to handle a similar event(s) in the future. Furthermore, it also allows the analyst to gain specific insight into what actions are needed to ensure the best possible future result.
To gain this comprehensive assessment, analysts employ neural networking or teaching a computer to learn to carry out a task by analyzing examples performed by humans. A prescriptive analysis is frequently utilized to complement different options to real-time consumer needs.
Implementing Analytics By utilizing one or more of these types of analytics, business owners can gain a deeper understanding of internal workflows, financial information, and how to make improvements for the future.
It allows analysts to drill down into past information to gain a comprehensive understanding of intricate patterns that may not be noticeable.
With developments in data analytics solutions and machine learning, companies have access to tools that can perform much of this drill-down work for them. As a result, organizations can minimize inefficiencies, tweak marketing campaigns, attract new customers, and improve future financial choices.
The Importance of Business Analytics
Business analysts have to simultaneously employ the best analytics approach to improve real-time decision-making while ensuring they can collect and manage high quantities of complex information.
Consistently seeking to improve each of the four types of analytics to gain the most benefits is the primary goal of organizations that use a business intelligence solution.
The value gained from data mining and machine learning techniques continues to be essential for business growth across all industries. Reasons for this include-
Improves performance- Data analysis provides a comprehensive picture of which elements of the company are working and what areas need improvement. Owners can then continue pursuing the elements that work and adjust the ones that don't to improve overall operational performance.
Ensures accurate decision-making- Data business analysis tools allow companies to stay up-to-date with the market by assessing and revealing customer feelings towards the business. Also analyzes the competition's market to improve the organization's outreach efforts.
Decreases risks/disruptions- Provides valuable insights so companies can make the correct decisions based on performance and customer patterns.
Encourages innovation- Provides the required customer data so organizations can know exactly what the market wants and need from them.
Delivers essential insights- Demonstrates how the company is performing with customers in real-time by collecting social media and website data.
Advantages and Disadvantages of Business Analytics
If used properly, analytics can streamline operations, decrease inefficiencies, and increase operational effectiveness.
Unfortunately, many businesses don't know how to harness their available collected data to their advantage. Furthermore, a data analyst may use inaccurate and irrelevant data sources, contributing to unreliable insights and poor decision-making.
Organizations must have a comprehensive understanding of how to use data mining and machine learning techniques to develop insights and reap the benefits of analytics.
If carried out properly, a data scientist can employ business analytics to-
Monitor progress- Evaluating information allows management to clearly understand what to expect in the workforce. It ensures the findings from analytics are shared with workers on a report or dashboard screen. Because workers will be better informed and held accountable for their actions, everyone is more productive.
Increase efficiency- An analytics data tool can collect large quantities of information very quickly and display it in an understandable way to increase the chance of everyone meeting organizational objectives.
Stay informed- Analytics can be used externally to better understand customer behavior and needs. This allows the organization to increase customer satisfaction and reach out to a new market to stay ahead of the competition.
Lack of available information- Many analysts only give their insights to top-level executives and managers, doing a disservice to workers and the organization as a whole. If everyone has access to analytics then each department knows exactly what is needed to meet personal and company-wide goals.
Takes time to work- Many companies are impatient that expensive BI tools are not delivering a return on investment. Analytics models increase accuracy over some time rather than overnight. To pinpoint patterns and insights, there must be access to long-term and quality historical information.
Delivers inaccurate results- If the data sources are poorly constructed or irrelevant to solving business needs, the insights gathered from a BI tool will be subpar. The company must ensure its data sources are accurate, reliable, and high-quality before employing analytics software.
The Relationship Between Business Analytics and Business Intelligence
Business intelligence is the procedure of collecting and analyzing operational information. BI generates a list of real-time metrics so users can know exactly what is occurring in each department. Management typically creates performance benchmarks to increase productivity and efficiency.
Typically, dashboards are the primary tool utilized to collect, manage, and track this information. Employees and management can access performance results in real-time to improve day-to-day decision-making processes.
The Rise of BI and BA:
Up until 1998, data was only controlled by IT departments
In 2006, Amazon created Web Services, which marked the beginning of cloud-computing
In 2010, Microsoft embedded Power Pivot into Excel, which finally made BI accessible to ordinary business users
In 2007, Apple introduced the iPhone, which opened up the door for mobile BI
Similarities and Differences Between BA and BI
BI prioritizes descriptive analysis by providing an assessment of historical information and current data to show what happened in the past or what is happening currently.
On the other hand, BA utilizes a combination of data mining and machine learning tactics to analyze historical information to predict future outcomes. It is mostly used to improve future decision making, not current decision making.
While both techniques use data to improve decision-making, BI tends to deliver less detailed results. BA drills further into information to know exactly what will happen in the future given past results.
Employing Both BI and BA
Organizations can gain the most benefit from both by utilizing a combination of BI and BA.
For illustration, an entrepreneur sells soaps from an online shop. A BI tool collects data to provide a report of the historical and current performance of the company. It shows that 600 vanilla soaps were sold in the last week, which is a 50 percent increase. Because of these insights, the entrepreneur decides to increase the production of vanilla soaps to meet demand.
On the other hand, analytics would ask why there was a spike in vanilla soap sales. The entrepreneur would mine collected website data to learn that the vast majority of the spike of vanilla soaps stemmed from a social media post created by a well-known health/beauty blogger in the community.
These BA insights prompt the entrepreneur to send more soaps to other health/beauty bloggers to try and repeat this month's results. The entrepreneur would utilize the previous sales data to estimate how many soaps to make and how many supplies are needed to meet demand if all goes according to plan.
Both a BI and BA tool can tell the entrepreneur valuable information so he/she can make better business decisions. While a BI tool helps deliver part of the story, BA delivers the totality of the story.
It drills down into the details to ensure the outcome most accurately meets business needs. While both are useful, business analytics is better at ensuring that resources are used wisely.
How to Manage Business Analytics for Growth
Utilizing software solutions and analytics tools can help to increase the chance of overcoming challenges and pinpointing inefficiencies.
Without quality data sources, and a clear understanding of how to perform analysis, it's very difficult to manage business analytics for growth in a large organization.
Furthermore, it's essential to know exactly what the business requirements are and what goals exist for the next few years. Only then can KPIs be chosen and a BA tool employed to monitor progress and growth.
Here are the best practices for managing business analytics for growth-
1. Project Management
Analyzing and measuring project management information is critical to ensure a task is streamlined from start to finish. Leveraging employee productivity when it comes to tasks/workflows allows an organization to save money, time, and resources.
Make certain to use metrics that monitor and track task due dates to hold involved workers accountable until the activity is completed. Also, track the time needed per task to manage timeline expectations and ensure accurate/profitable time estimates.
Finally, utilize business intelligence metrics to perform checkups during different stages of the task to improve the quality of the outcome.
2. Sales Cycle Management
To find potential areas of growth in the sales cycle, many business track the number of new customers, sales funnels, the top-selling plans, the top-selling reps, the best markets, and whether sales have met preordained expectations.
Knowing which sales reps perform the best help to make better management decisions while assessing what the sales goals are helps everyone to achieve them. Tracking the lead to customer conversion rate helps to ensure the best hiring practices and marketing choices are put in place.
Finally, understanding where customers come from allows sales team members to adjust sales strategies and receive a return on time and effort.
3. Financial Management
Properly tracking financial data allows owners to ensure proper cash flow management. Use metrics that track business expenses to know where cash is going and if it's being used efficiently.
Managing invoices is also essential to avoid having overdue customer accounts, which results in wasted money. Tracking company revenue allows the organization to make improvements in inefficiencies and ensure goals are met regularly.
Finally, managing departmental budgets at a view is essential to make predictions and know when to cut back on spending.
Interesting Facts About the Sales Cycle:
92% of all customer interactions occur over the phone
30-50% of sales go to the vendor who replies first
80% of sales require 5 follow up calls after the initial meeting
Thursday is statistically the best day to prospect
Key Takeaways of Business Analytics
In conclusion, here are the key points to remember about business analytics-
Business analytics is the process of using historical and current data to predict future outcomes, to improve decision-making across an organization.
Advantages of an analytics program are that it monitors progress, increases efficiency, and keeps the organization updated with real-time information so they can remain competitive. Improper data science business analytics includes a lack of employee access to information, takes a long time to work, and low-quality data can deliver poor results.
BI focuses primarily on descriptive analytics while BA focuses on predictive analytics. Both are useful to ensure better decision-making, but BA is more critical.
To optimize a business analytics program for growth, use metrics to monitor project management, sales management, and financial management.