In today’s business landscape, data has emerged as one of an organization’s most critical assets. Everything a company does—every interaction, transaction, and digital footprint—provides insights, and it is possible to analyze these insights to make better decisions. Business analytics and data-driven decision making are predicated on this premise of using data to make better decisions, and are changing how organizations operate, compete, and grow.
Regardless of whether you are a small business owner or managing an enterprise with global reach, understanding how to leverage the big data landscape will fundamentally change the way you make decisions, increase efficiencies, and leverage competitors.
What is Business Analytics?
Business analytics is the technologies, skills, and practices for exploring and analyzing past and current data to drive business planning. Business analytics represents a combination of statistical methods, predictive modeling, and data visualization tools to help uncover patterns, predict future outcomes, and support decision making processes.
Business analytics falls short of analysis and indeed replaces it. In contrast to traditional reporting that merely reflects what happened, business analytics will assist you in understanding why this happened and forecast what will happen next.
What is Big Data?
Big data refers to extremely large data sets that cannot be processed by traditional data management systems. Big data might arise from a multitude of sources such as:
Interactions on social media
E-commerce transactions
Customer survey results
Internet-enabled devices and sensors
Behavior on websites and web analytics
The distinguishing characteristics of big data are often described as the “3 Vs”:
Volume: Very large amounts of data
Velocity: The speed at which data is generated
Variety: Different types and sources of data.
Taking advantage of big data is the first step to data-driven decision making.
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The Significance of Data-Driven Decision Making
Data-driven decision making (DDDM) refers to the method of making decisions based on trust-worthy data rather than an intuition or a feeling. When organizations embrace DDDM, they can:
Mitigate risk
Enhance the customer experience
Optimize the operation
Adapt more quickly to the market
Foster innovation
According to a case study conducted by McKinsey, organizations that employ a data-driven approach are 23 times as likely to gain customers, 6 times as likely to keep customers, and 19 times as likely to be profitable.
How can Business Analytics Help Make Better Strategic Decisions.
- Discovering Trends and Opportunities
Analytics will reveal trends as they emerge regarding consumer behavior, industry movement, and internal processes – enabling organizations to respond quickly to trends. For example, retailers now have access to information on customer purchases and identify high moving items and increase inventory levels of those items.
- Driving Operational Efficiency
Leverage workflow, supply chain, and performance data to find inefficiencies in processes and eliminate bottlenecks. The end result is quicker delivery times, lower costs, and more efficient use of human resources.
- Delivering a Personalized Customer Experience
Organizations can leverage data on consumers to improve marketing campaigns, recommend products and services, and enhance customer service engagements (e.g., amazon, netlix)
- Reducing Risk
Analytics will uncover theft, anticipate breakage of costly equipment, or identify credit risk. For example, banks rely on predictive models to evaluate loan applications and minimize the risk of default.
Real-world Examples of Data Driven Decision Making in Action
Amazon
Amazon leverages big data to enhance everything, from pricing to logistics in its warehouses. With the benefit of real-time analytics, they can monitor the state of their inventory, predict when stock is about to run out, and suggest products to consumers based on prior visits.
Netflix
Netflix acquires data related to the audience viewership for much of its programming in order to inform its content strategy. For example, the successful series House of Cards was given the green light based on data indicating that the market had a high demand for political dramas, a particular viewing demand for director David Fincher films, and for actor Kevin Spacey’s work.
Starbucks
Starbucks utilizes location analytics to help decide where to open new coffee shops. When choosing stores, Starbucks analyzes data on foot traffic, demographics, and consumer preferences to select a strong location.
Amazon, Netflix and Starbucks leverage Big Data to make decisions from pricing to how they manage payments. In addition to data issues, there are plenty of other barriers to leveraging analytics when it comes to the workplace:
- Competing Priorities
The data analytics process is often deprioritized in favor of the operational considerations which seem more urgent.
- Company Culture
Changes might take time to implement given how deeply ingrained company cultures can be, especially with regards to behaviors in using data analytics.
- Multiple Data Sources
More data inputs can lead to multiple versions of the truth, fragmentation, and confusion across varying data sources.
- Lack of Experienced Data Talent
It is difficult to find qualified data analytics professionals to find the right talent that can understand complexities and translate results in ways employee and peers could understand.
- Privacy and Security
As analytics expand, a greater focus on data security will also be needed, from planning properly for data storage, to being aware of and keeping track of privacy laws in different countries.
Tools and Technologies Behind Business Analytics
There are various tools available to help organizations implement data-driven decision-making:
- Data Visualization Tools
Tableau
Power BI
Google Data Studio
These tools translate complex data into visual representations such as charts and dashboards which are easier to interpret.
- Statistical Software
R
SAS
Python (with pandas, NumPy, scikit-learn)
These tools are used for predictive modeling, regression analysis, and machine learning.
- Data Warehousing
Amazon Redshift
Google BigQuery
Snowflake
These are cloud-based data warehousing tools that store and manage large amounts of data, enabling analysts to query data to support their analytical activities.
- CRM and ERP Systems
Salesforce
HubSpot
SAP
These platforms provide analytics tools that easily enable organizations to analyze sales, customer interactions, and business processes as a part of whatever the embedded processes are.
Real-Time Analytics in Business
Real-time analytics offers profiles desired by an organization. It combines customer attributes, organizing the information in ways that looks at what each customer is viewing.
Retail
Many retailers use heatmapping to profile interactions inside of stores.
Ecommerce
These sites provide behavior analytics, many are able to track click-through rates, measure engagement on website pages, and gather insights on product preference.
Mobile Applications
Similar to ecommerce websites, mobile applications provide behavior analysis on consumer interactions as it relates to app events, engagements on push notifications, and purchases.
Challenges in Using Big Data and Analytics
Analytics can be used to identify trends and influence a person’s purchasing decision. Business intelligence suppliers offer both analytics and trends in measurable ways, this depends on how organizations communicate with customers.
Despite the potential of using business analytics, there are challenges that a company may need to face:
Data Quality
Issues When data is bad or incorrect, the decision based on incorrect data will also be bad. Businesses want to ensure that the data is correct, accurate, consistent and complete.
Skills Gap
There is a significant need for data scientists, data analysts, and BI roles. The only way to fill this skills gap is to either train your employees or hire qualified candidates.
Data Privacy and Security
More data equals more responsibility. Organizations need to comply with data protection laws (e.g. GDPR) while also protecting sensitive information.
Cost and Infrastructure
Data and organizations can be expensive to develop, especially for small businesses. However, development has never been easier with the rise of cloud-based solutions and SaaS tools.
Getting Started with Business Analytics
If you are new to data-driven decision making, here are some simple steps to get started:
Step 1: Define Goals
What are you hoping to achieve? Whether it’s higher sales, lower churn rates, or higher efficiency, it is important to be clear about your goals.
Step 2: Collect & Analyze Data
Start with the data that you have on hand. This could be anything from sales reports and web analytics to customer feedback. As quickly as possible identify new sources of data to use.
Step 3: Choose The Right Tools
Begin using tools that are user friendly such as Google Analytics, Excel, or Power BI. As your use of analytics matures, consider using more advanced tools.
Step 4: Train Your Team
Consider providing your team with basic training on how to read data, visualize results, and explore different BI tools. Instill a ‘data first’ mentality within the team.
Step 5: Use Data Frequently
Routine reporting, KPI tracking, and adapting based on reporting, allows you to develop the system within your organization. The more you begin to rely on data, the more intuitive your decision-making process becomes.
The Future of Business Analytics
As AI and ML continue to see rapid advances, the future of business analytics is extremely bright. Predictive and prescriptive will be the norm, meaning businesses will not only be able to predict where something might result patterns, but will also be able to prescribe actions to be taken in real time to ensure the business is on the right outcome.
Automation, natural language processing (NLP) and real-time streaming will be relied on more and more by businesses looking to make decisions in real time. Early adopters of data driven businesses will be the ones leading their industries.
Business analytics can also compliment with How Automation is Changing the Workplace to streamline decision making and boost performance.
Conclusion
Big data and analytics are transforming how businesses are making business decisions. Organizations big and small can leverage the power of data-driven decision making through the development of systems, tools and mindset to drive growth, improve customer experiences and remain relevant in a competitive, data driven world.
Business are not just reacting to trends as they were, but now they are also predicting and driving change. The sooner your business embraces analytics or business analytics the more competitive it will become in the digital age.
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