One Truth? using data in business
Do you know your data? TonkaBI has spent years perfecting their data analytics tool for those tricky analytical challenges you may face on a weekly basis. Have you ever been to a meeting and numbers and data are being thrown around with passion and belief, yet no one can agree if the numbers are right or if they are being interpreted differently?
Clean, correct, consistent, reliable information is what everyone wants especially when the business wants to be a data-driven organization. Data is fast turning into one of the most important assets that a company can have.
Where are you in the path to having one truth – clean, correct, consistent, reliable data? Below are a series of tests or obstacles that have to be overcome to get you along this path towards a business based on one set of data truth
Collecting Pool of Manual Data
The collection of data in this way is tedious and is a waste of your employees’ valuable knowledge and experience. At worst, it’s a grave mistake that puts the performance of your business in danger. Microsoft Excel makes it easy for anyone to do number crunching and produce something that looks good. But the world’s best-selling spreadsheet software has also contributed to the proliferation of bad data. A survey by Hawaii University found that close to 90% of spreadsheet documents contain errors, analysis of multiple studies suggests. “Spreadsheets, even after careful development, contain errors in 1% or more of all formula cells,” a professor stated and an authority on bad spreadsheet practices. “In large spreadsheets with thousands of formulas, there will be dozens of undetected errors.” These errors put your business at risk.
Multiple Sources of Data
Typically most data repository have information coming from a variety of places, from CRM tools, Sales, Websites, Public sources, IoT, HR, Accounts, etc. Typically these can quickly build up to 5, 10 and more for a company. The issue is the definition of each item may vary in the data and these separate pieces must first be checked against each other to ensure their consistency. To be useful, data should be integrated within a single centralized hub via a ETL (Extract, Transform and Load) process.
Inconsistently Built Reports
The manual processes within report delivery can affect more than data collection, they critically also result in bad reports that suffer from inconsistencies. Manually-built reports may be inconsistent in a variety of ways, including their authors(as people come and go), their intended audience, their error rate, the time it takes, their release schedule. On top of this different departments and people can have different standards for their reporting and this makes them fundamentally unreliable.
Microsoft Excel is a great tool and provides the standard productivity software, but it’s being used for much more than its creators intended, including complex business process management tasks. Excel simply isn’t appropriate for doing more complicated data analysis. Hours and Hours of wasted time is spent in organisations completing repeatable ‘reorts’ every month – collating board packs, sales pipelines, HR, accounts – its everywhere and it is an unseen and accumulating cost every month.
Data Standards and Best Practices
Companies struggle to break down departmental “data silos” and this leads to isolating the different parts of the business from each other. Companies need toaim for data integrity, adding consistency in terms of best practices for data collection and analysis. Without a standardized workflow for data processing, each division will withdraw into itself instead of sharing valuable information with other departments. Making production data freely available to marketing for example allows for better planning and knowledge sharing across the business.
Data at Speed
Collating data manually can add hours, days and even weeks into solving problems. As an example, lets look at building a complex monthly report – standard to most businesses. There is a cut off date lets say week 4; it then takes a week to build the report and publish it for a meeting in week 6 – this means the following: The data is up to 6 weeks old; at best its around 2 weeks out of date; the company may also have the current 2 weeks issues to deal with; its taken a week of time to prepare; its built in Excel so its prone to error; To get a different view or more data added in can take weeks and the problem repeats every month!
If you allow TonkaBI to build this process, the ‘report’ can be live every day and the period of the report or the view of the report is instant – allowing your valuable staff to move from report generation to problem identification and fixing.
tags #insurance #claims #dataanalytics #bigdata