EnJomo Analytics

📈 *EnJomo Analytics* 📉


*We are here for you, if you need assistance in any of the following:*

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🗂️Attachment reports
📊Business proposals
🗃️Plagiarism checks (Turnitin)
🙋🏻‍♂️Dissertation consultation

*📊 Statistical Data Analysis*
👩‍💻 *R-Studio, STATA, SPSS, EXCEL & LaTex*

Contact us on:

*WhatsApp:*
https://wa.me/message/OBU6XHFJ2YZJL1

*Website:*
https://enjomo-analytics.business.site

*Facebook:*
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*LinkedIn*
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wa.me

Why do people use Power BI?

For businesses who want more reporting power and analytical muscle than that offered by Excel, Power BI is the next level in business intelligence. With Power BI, businesses can assemble, scrutinize, and visualize data from across the company, giving them greater insight into their operations and performance, and allowing them to make more informed decisions based on real data.

Here are some of the key benefits of using Power BI:

--Businesses can input huge quantities of data into Power BI that many other platforms would struggle to process
--Built-in machine learning features can analyze data and help users spot valuable trends and make educated predictions
--Information can be visualized using powerful templates to allow businesses to better make sense of their data
--Power BI is cloud-based, so users get cutting edge intelligence capabilities and powerful algorithms that are updated regularly
--Powerful personalization capabilities allow users to create dashboards so they can access the data they need quickly
--Alerts can be set up on KPIs to keep users up to date important metrics and measurements.
--Power BI has an intuitive interface that makes it far more user-friendly and easy to navigate then complex spreadsheets
--The platform integrates with other popular business management tools like SharePoint, Office 365, and Dynamics 365, as well as other non-Microsoft products like Spark, Hadoop, Google Analytics, SAP, Salesforce, and MailChimp
--With data security a massive talking point for modern businesses, Power BI ensures data is safe, offering granular controls on accessibility both internally and externally

📈 EnJomo Analytics 📉


We are here for you, if you need assistance in any of the following:

📜Assignments
✨Research proposal
📂Dissertations
🗂️Attachment reports
📊Business proposals
🗃️Plagiarism checks (Turnitin)
🙋🏻‍♂️Dissertation consultation

📊 Statistical Data Analysis
👩‍💻 R-Studio, STATA, SPSS, EXCEL & LaTex

Contact us on:

WhatsApp:
https://wa.me/message/OBU6XHFJ2YZJL1

Website:
https://enjomo-analytics.business.site

Facebook:
https://www.facebook.com/enjomoanalytics/

LinkedIn:
https://www.linkedin.com/company/enjomo-analytics-inc/

📈 EnJomo Analytics 📉


We are here for you, if you need assistance in any of the following:

📜Assignments
✨Research proposal
📂Dissertations
🗂️Attachment reports
📊Business proposals
🗃️Plagiarism checks (Turnitin)
🙋🏻‍♂️Dissertation consultation

📊 Statistical Data Analysis
👩‍💻 R-Studio, STATA, SPSS, EXCEL & LaTex

Contact us on:

WhatsApp:
https://wa.me/message/OBU6XHFJ2YZJL1

Website:
https://enjomo-analytics.business.site

Facebook:
https://www.facebook.com/enjomoanalytics/

LinkedIn
https://www.linkedin.com/company/enjomo-analytics-inc/

wa.me

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in several different ways. It can help to group these types of analysis into four categories commonly used in the field. We’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.
Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.

Descriptive analysis answers the question, “what happened?”
Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”
Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”
Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

What Is Data Analysis?

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.