With the advent of data-spitting technologies, the opportunities to collect information from within and outside the organization grow tremendously. From edge sensors and social media platforms to customer relationship management (CRM) and enterprise resource planning (ERP) software—every file is generated in different formats wherein each data…Continue
Added by Jessica Anderson on May 24, 2021 at 6:31am — No Comments
Accurate and reliable data in a CRM (Customer Relationship Management) system is essential if you want to use the right channels to connect with customers and potential clients at the right time.
You can refer to an account profile using the stored data whenever necessary. The sales team also rely on CRM data to gasp sales opportunities.…Continue
Added by John Sema on March 22, 2021 at 7:00pm — No Comments
Data accuracy is the biggest challenge for many businesses, having accurate data is useful in all its stages to use. Data results in inaccuracies when it is created, collected or during the clean-up, or when being stored. The inconsistencies from any of the sources make the data useless or less…Continue
Added by Indhu on March 12, 2021 at 12:22am — No Comments
Here are some of the things a company can do to improve the quality of the information it collects:
1. Data Governance plan
A good data governance plan should not only talk about…Continue
Added by Indhu on October 26, 2020 at 11:30pm — No Comments
Mergers & acquisitions happen when companies believe they are more valuable together than when operating separately. The companies join workforces, systems, infrastructure, and data to become a new, more powerful, more valuable, more effective entity. That is only until they realized they overlooked or underestimated the key issues with data, IT infrastructure & integration plans. In fact, most merger and acquisition plans fail miserably because of data…Continue
Added by Farah Kim on May 26, 2020 at 8:30pm — No Comments
In today's hyper-competitive business world, B2B & B2C businesses rely on data to achieve an edge in their markets. However, many companies do not realize that customer data decays rapidly and create challenges in achieving business goals. In the postmodern era, customers simply change base too often, and to maintain a complete and accurate record of their whereabouts is a challenging task. Dirty data not only damages your credibility but also costs you money…Continue
Added by Chirag Shivalker on March 4, 2020 at 9:24pm — No Comments
Outdated, inaccurate, or duplicated data won’t drive optimal data driven solutions. When data is inaccurate, leads are harder to track and nurture, and insights may be flawed. The data on which you base your big data strategy must be accurate, up-to-date, as complete as possible, and should not contain duplicate entries. Clean data results in…Continue
Added by Favio Vázquez on August 18, 2017 at 8:00am — No Comments
Do you often go with gut feeling rather than data and insights? Is your data stored in separate databases, in different formats with different values? We all have bad habits and some are a little hard to kick. However, if there is one you must break, it is surely to make your bad data habits a thing of the…Continue
Added by Martin Doyle on March 6, 2017 at 2:30am — No Comments
Whatever your business sector, data is your most valuable asset. Along with the machinery and stock you hold, data and insights hold the key to profit and growth. But it has the unique ability to unite every department, and every function. It can reveal problems in processes, drive productivity among your staff and ensure everyone is ‘singing from the same hymn…Continue
Added by Martin Doyle on April 6, 2016 at 3:30am — No Comments
The fundamental assumption in many predictive models is that the predictors have normal distributions. Normal distribution is un-skewed. An un-skewed distribution is the one which is roughly symmetric. It means the probability of falling in the right side of mean is equal to probability of falling on left side of mean.
This article outlines the steps to detect…Continue
There are always two aspects to data quality improvement. Data cleansing is the one-off process of tackling the errors within the database, ensuring retrospective anomalies are automatically located and removed. Another term, data maintenance, describes ongoing correction and verification – the process of continual improvement and regular checks.