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Tag: self-service analytics

Data Quality Management

Dirty Data – Hygiene Etiquette

If you’ve ever analyzed data, you know the pain of digging into your data only to find that the data is poorly structured, full of inaccuracies, or just plain incomplete. But “dirty data” isn’t just a pain point for analysts; it can ultimately lead to missed opportunities and lost revenue to an organisation.  Gartner research shows that the “average financial impact of poor data quality on organizations is $9.7 million per year.”

The amount of time and energy it takes to go from disjointed data to actionable insights leads to inefficient ad-hoc analyses and declining trust in organizational data.

A recent Harvard Business Review study reports that people spend 80% of their time prepping data, and only 20% of their time analyzing it. And this statistic isn’t restricted to the role of the data stewards. Data prep tasks have bled into the work of analysts and even non-technical business users.

Enterprises are taking steps to overcome dirty data by establishing data hygiene etiquette:

  • Understand your data location, structure, and composition, along with granular details like field definitions.

Some people refer to this process as “data discovery” and it is a fundamental element of data    preparation. Confusion around data definitions, for example, can hinder analysis or worse, lead to inaccurate analyses across the company. For example, if someone wants to analyze customer data, they may find that a marketing team might have a different definition for the term“customer” than someone in finance.

  • Standardize data definitions across your company by creating a data dictionary.

This will help analysts understand how terms are used within each business application, showing the fields are relevant for analysis versus the ones that are strictly system-based. Developing a data dictionary is no small task. Data stewards and subject matter experts need to commit to ongoing iteration, checking in as requirements change. If a dictionary is out of date, it can actually do harm to your organization’s data strategy. Communication and ownership should be built into the process from the beginning to determine where the glossary should live and how often it should be updated and refined.

  • Data cleansing prior to imports

You need to prepare your data before even thinking of importing it in your system.  Every organization has specific needs and there is no ‘one size-fits-all’ approach to data preparation. A self-service data preparation tool allows people to see the full end-to-end process, seeing potential flags earlier on—like misspellings in the data, extra spaces, or incorrect join clauses. It also increases confidence in the final analysis.

  • Hands off!!

Keeping your hands out of the data in regular use increases the chances of it keeping clean. Introducing a little dirty data to a system will compromise an entire data set and your little bit of dirty data has suddenly created a lot of dirty data. Cleansing the mess is a far far bigger job than making sure the data is clean before importing it.

  • Invest in a self-service business intelligence tool

Adopting a self-service data prep across an organization requires users to learn the ins and outs of the data. Since this knowledge was historically reserved for IT and data engineering roles, it is crucial that analysts take time to learn about nuances within the data, including the granularity and any transformations that have been done to the data set. Scheduling regular check-ins or a standardized workflow for questions allows engineers to share the most up-to-date way to query and work with valid data, while empowering analysts to prepare data faster and with greater confidence.

Data hygiene should be a top concern in organisations. Devoting some resources to ensuring that the data you’re basing decisions on is complete and accurate is a smart investment, because dirty data is costly in so many ways. To get the most and best use out of your data, you need to take the time to ensure its quality is sufficient and that data used by different departments is integrated. This gives you the most complete and precise customer view, so you can make smarter decisions and maximize your return on investment.

Self-service Analytics

Track profit, loss with an intuitive CFO dashboard

This CFO dashboard combines complex profit-and-loss data into one page that’s anything but. The top two views provide an overall picture of quarterly and yearly performance over the past three years. The views include key financial measures such as net sales, net profit, and net profit margin. Whether you want to view your numbers according to region, channel, customer segment, or product category, the results are right at your fingertips.

Click on the link below and be amazed by this dashboard . It’s just a click  away!!

https://www.tableau.com/solutions/workbook/cfos-overview-business

 

Happy to have a chat with you to share ideas, discuss opportunities or even prepare a demo for you . Contact us on info@businesslab.mu.

Business Intelligence

Key Benefits Of Tableau Software In The Retail Sector

 

Tableau helps people see and understand their retail data no matter how big it is, or how many systems it is stored in. Quickly connect, analyze, and share insights to reveal hidden opportunities that impact each sale, and your entire organization. With a seamless experience across PC, tablet, and smartphone, ask and answer deeper operational questions with expressive, interactive dashboards—no programming skills required.

 

Key Benefits of Tableau in the Retail Sector:

Product Availability

Tableau can help retailers  to solve the problem of having the correct product, at correct inventory levels, in the correct stories. By analyzing product availability by category, supplier, day, and store region, retailers can identify gaps in efficiency and interactively drill into the details.

Promotional Optimization

More than half of all retail sales are made during promotions, so it’s important to be able to visualize data during each phase – pre-promotion, the promotional period itself, and post-promotion. Tableau helps you create dashboards showing  exactly what’s happening throughout the promotional cycle so they can prepare for availability issues by highlighting potential inventory stock shortages.

Store Operations

Store operations dashboards can provide rich insights to the corporate office, and region/ store managers about performance and execution. With store operations dashboards, managers are able to better benchmark the performance of stores in a region against metrics like Sales, Year Over Year growth, Traffic, Average Transaction Value and Units Per Transaction. They can also drill down to unique store level operations data like weekday vs weekend sales, product department performance, compare space productivity within the store and evaluate top selling brands.

Merchandising Assortment

Category managers need to make profitable decisions around products that customers are demanding. In order to do that, merchandisers need to quickly analyze demand data by sales and profit margin performance by departments, sub-categories and brands. With merchandising assortment dashboards, managers can gain a quick overview of relative sales performance of sub-categories, share of private label and national brands, and categories that can contribute up to 80% of sales. They can also get richer insights on the attributes that lead to higher conversion such as product type, colors, shape, and styles.

Brand Health

For most consumer product goods companies, brand perception is critical to the success of their business. Executives at large CPG companies need insights into how consumers perceive the company brand, for the benefit of derived utility and price.

A brand health dashboard can show the benefit vs price effect by deep diving at the sub brand level . Combining this with profitability and price elasticity of the brand, executives can decide on allocation of additional marketing dollars to gain Category leadership for these brands and push the benefit perception higher for higher profitability brands.

Happy to have a chat with you to share ideas, discuss opportunities or even prepare a demo for you . Contact us on info@businesslab.mu.