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Winning Strategies for Successful Analytics Projects

For businesses to realize the full potential of their data assets, they must leverage data analytics to enhance operational efficiencies, drive growth, improve the customer experience and optimize business efficiency. The most lucrative companies derive analytics from their data to evaluate the competition, identify emerging business trends and ultimately, gain a competitive advantage.

Analytics

Despite the massive opportunities buried in mountains of enterprise data, even the most experienced organizations fail to realize the full potential of data analytics. Across every industry, business are faced with obstacles as they attempt to convert data into insights, such as:

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Poor Data Quality

 Data is arguably an organization’s most valuable asset. However, if the data’s quality is in doubt, business users across the enterprise won’t trust the data, let alone leverage it for insights. And if they do rely on poor quality data, the adverse effects of faulty insights can reverberate across the business. Bad data leads to bogus intelligence, negative customer experiences, lost revenue and missed opportunities.

To create trusted data, organizations need data quality and profiling controls to validate data’s integrity. Checks for completeness, consistency and conformity ensure business users are leveraging high-quality, relevant data. Balancing and reconciliation controls assure data arrives accurately and on time. Timeliness rules monitor file delivery and flag any late or missing files. Reason ability checks affirm that data is within expected thresholds.

By integrating a broad range of data quality checks within a comprehensive data governance program, businesses can foster data quality initiatives to improve and score their data assets.

 Lack of Business Alignment and Data Governance

 A primary goal of data governance is to establish trust in data and leverage it as an enterprise-wide asset, and data integrity is a critical part of these efforts.  As data travels through the data supply chain, it is exposed to new processes, uses and transformations, which can significantly degrade data integrity. By establishing a comprehensive data governance strategy and incorporating data integrity checks, businesses prevent downstream data quality issues from proliferating, and help build business user trust in enterprise data.

Data governance also encourages collaboration to align data understanding among IT and disparate lines of business. When different departments work together to define and document data, it establishes a common understanding of data assets and eliminates business user confusion as they leverage data for business purposes.

High quality, well-governed data lays the groundwork for meaningful analytics. The next step is empowering business users to quickly prepare and analyze enterprise data to generate actionable business intelligence.

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 Ineffective Data Preparation and Analytics Strategies

 Many organizations still rely on IT for data analytics, leading to backlogged requests, overworked resources and belated, outdated results. Delays are exacerbated by highly technical tools that take days or weeks just to prepare data for analysis.

But increasingly, companies are implementing enterprise data governance integrated with data quality and analytics. Self-service data analytics solutions are the latest evolution in data preparation and analytics, enabling business users to take an active role in data analysis. By educating these data consumers about the source, usage and meaning of critical data, and equipping them with the ability to engage in analytics, organizations can dramatically increase the quality and volume of their business intelligence, helping them achieve objectives and generate ROI.

 By leveraging data governance to ensure data quality, build data trust and literacy and empower more data consumers to perform data analysis, organizations can improve productivity and significantly lower overall costs. When different lines of business can self-service their own data needs, they can leverage advanced analytics to speed up time to insights, and ultimately, increase profit.

What’s It Actually Like To Be A Data Analyst?

Data Analyst We touched upon a range of topics; from why he got into data analytics, to how his background helped him in his current work. We also got a fascinating insight into his particular role within his company, and the tools he uses on a daily basis.

Data Analyst

 

If hearing about Radi’s life as a data analyst has made you think ‘I could do this job!”, then why not get a taste by actually doing some data analytics? As part of our Intro to Data Analytics Course, you’ll focus on Microsoft Excel—a key tool in analytics— and get a crash course in analyzing data over 10 exercises.

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Our questions for Radi

  1. A background in data science
  2. A day in the life of a data analyst
  3. A career in data analytics and the future

1. A background in data science

What drew you to the world of data analytics?

Before I started working in the field, my understanding was that data analysis is used by companies to target specific consumers, or as a way for companies like Facebook and Google to “enhance the user experience” by targeting adverts based on browsing habits.

My opinions changed once I started working for my current company, which uses data analysis for a good cause. My company analyzes DNA related data in young people to predict future diseases such as Alzheimer’s, Diabetes, Crohn’s, and many more. This really changed my perspective on data analysis and made me feel like I’m making an actual difference in the world.

How has your background in computer science helped you?

To work as a data analyst, you need to master at least one of the main programming languages. The analysis for our medical data is done by an AI software that we built and continue to improve using two programming languages: R and Python. These are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets. They’re both standard languages in data analytics, and my computer science studies in university certainly gave me a good grounding in the languages from which I’ve built on.

2. A day in the life of a data analyst

Can you walk us through a typical day at work?

Usually, my day doesn’t start until I’ve finished my first cup of coffee. It’s not about the caffeine as much as it is a ritual of getting into “the zone” before I start working with massive amounts of medical data.

A typical day usually includes, but is not limited to, meetings with the analytics team to discuss the tasks of the day and brainstorm for possible solutions. When everything is clear, I start working on the data. Analyzing data consists of three main tasks: gathering the data, cleaning the data, and finally processing the data.

Depending on the problem I’m working on, gathering data is usually the most simple part of the process, because the medical databases I work with are easily accessible—and I don’t have to worry about searching for it. Cleaning the data, which is the next step, simply means going through the data and trying to understand it, making corrections where needed such as moving outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial in order for me to start processing the data.

The data processing part of the process is where I get to use my programming skills, which I use alongside several different data tools. I use these skills and tools to analyze the work and come up with solutions for the problem at hand.

What are a data analyst’s responsibilities?

My role involves:

  • Gathering data
  • Cleaning data
  • Processing data
  • Producing reports
  • Spotting patterns
  • Collaborating with others and setting up infrastructure

How much of a role does data cleansing play in your processes?

Cleaning data is a very important process because you need to recognize which data should stay and which should not. Including incorrect data while processing it might give you the wrong results, which in turn can lead to coming up with the wrong solutions. You then have to repeat your work, which is a waste of your time.

How often do you meet with stakeholders to discuss business needs and new things to analyze?

Personally, I don’t have to meet with any stakeholders, that’s the job of my team members. The only people I collaborate with are the analytics team because we need to keep each other updated on how things are going.

What are your experiences with Excel?

While Excel is a powerful tool in data analysis, it still has a lot of serious limitations. Excel cannot handle datasets above a certain size, and does not easily allow for reproducing previously conducted analyses on new datasets. The main weakness of such programs is that it was developed for very specific uses, and do not have a large community of contributors constantly adding new tools. Which is why I prefer using and R and Python.

Tell us more about R and Python!

R and Python are the two most popular programming languages used by data analysts and data scientists. Both are free and open source. R is used for statistical analysis, and Python is a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are both godsends.

To go into a bit more detail, R is one of the most frequently used tools in data science and machine learning. Over the last few years R has become the golden child of data science. It’s used frequently to unlock patterns in large blocks of data and was designed by people like me, statisticians, to make our work easier. It one of the most must-know programming languages in the field of data analytics and data science.

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3. Having a career in data analytics and what the future holds

Where can you see data analytics heading in the future?

Data analytics IS the future, and the future is NOW!

All the actions you do on your computer, smartphone or tablet are recorded and collected by a data analyst somewhere who is trying to make their business flourish. That’s right—every mouse click, keyboard button press, swipe or tap is used to shape business decisions. Everything is about data these days. Data is information and information is power. I don’t want to get political, but the more ‘data’ you have on someone, the more you can control their lives.

Where do you see yourself in 5 years?

Honestly, I don’t know. A year ago, I couldn’t have imagined living and working in Berlin, but here I am, and it’s the same regarding my work. As long as the effort I put into my work is worth the reward then I will keep doing it until it’s not. And fortunately, my education in computer science and graphic design will always be in demand, so I try not to worry about the future and just enjoy the moment for now.

What does career progression in data analytics look like?

There are various professional possibilities that people in data analytics can aim for.

Some of these possibilities are, but not limited to:

  • Data Management Professional
  • Data Engineer
  • Business Analyst
  • Machine Learning Researcher/Practitioner
  • Data-oriented Professional

But of course, each one of these categories can branch out to subcategories which can open up more career opportunities.