Answer the questions from the checklist rating each of the 7 common data problems on a  scale of 1 to 5 based on how confident you feel each problem is being dealt with.

Your responses will then be evaluated by one of our data scientist consultants, who will get in touch to arrange a suitable time to discuss them and help you develop a plan of action to address any of the issues or gaps you have highlighted.

The free assessment will be no longer than 1 hour and will be carried out as a virtual meeting with you and or your team.

Our data scientist consultant will also share with you several strategies, which are key to improving the quality and value you can get from data, including the benefits they have helped our clients achieve. 

We hope you find our data evaluation and assessment useful and enjoyable and we look forward to reviewing the results from your completed checklist with you soon.

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* 1. Classic needle in a haystack problem
One of the biggest challenges faced by businesses handling data is there is often too much of it. This creates the classic needle in a haystack problem. It's difficult to get insights from a huge lump of complex data and trying to find a signal in the noise can be really time-consuming. Businesses should learn to approach data more carefully and scientifically and a good place to start is by knowing your customers and the problems they want you to solve.

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* 2. Data in SILO’s
Too often data lives in separate, disparate units, which makes it difficult to gather insights because it simply isn’t integrated. Data in SILO’s is the reason you have to crunch numbers to produce monthly sales reports. They’re the reason why C-level decisions are made at a snail’s pace. They’re the reason why your customers are looking elsewhere because they don’t think their needs are being met. The only way to change this is to eliminate data silo’s and bring all your data into one place.

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* 3. Unreliable and inaccurate data
According to a report from Experian, 75% of businesses believe their customer contact information is incorrect and 80% of the work data scientists often do is cleaning up the data before they can take a look at it. There are lots of great tools and processes to help you clean up and maintain your data, but they tend to centre around practices such as; removing duplicates, setting standards to verify data, keeping your data updated and ensuring your employees follow company-wide data entry standards such as first name last name.

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* 4. Legacy systems – no real-time data
Many legacy on-premise systems only provide batch or historical data, which makes it difficult to get ahead of the game. It’s much easier to obtain relevant, real-time data from cloud-based systems so you can analyse it quickly, surface actionable insights and drive them back into operational systems affecting events as they’re still unfolding. The ability to catch people or things ‘in the act’, affecting the outcome will give you a clear advantage over other organisations.

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* 5. Lengthy manual processes
This is a particular problem for organisations with value chains comprising of complex networks of customers, people and systems. Planning how to deliver value to your customers can often take many weeks and rely on a multitude of systems, data inputs, manual processes and external data sources. By the time the requirement gets into the hands of delivery, it would be too late to make any insightful decisions that would benefit customers, because the data has lost its value. If businesses want to get real value from their data, they must learn how to re-engineer core business processes too.

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* 6. Knowledge, skills and capabilities
If you don’t have the right resources with the capabilities and knowledge, you will find yourself on the back foot with your data. There needs to be a blend of business knowledge as well as technical capability, such as data engineering, where you can work out how to get data from its source so it can be used. Data scientists are then the people, who will get you on the front foot, by using analytical techniques such as data mining, AI or predictive analysis. The earlier you can glean insights from your data, the lower the costs and the greater the value to your customers. 

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* 7. Low level of data maturity
If you want to get the most out of your data, you need to understand where you are in your data maturity. Many business’s data lives in the heads of individuals or multiple systems and databases, no reports are standard and there is a lack of integration. Businesses with low levels of data maturity, should standardise their reporting and eliminate things that add no value. However, data-driven businesses have clarity and speed accessing data across multiple departments and systems and are able to correlate and manage their data, often in real-time,  delivering deep insights, which can be acted on instantly.

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* 8. Insert contact details below

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