There’s always more engineering up front than you might anticipate

As a result, businesses get frustrated because value is slow to come

• Underestimating this is one of the primary causes Data Scientists leave their jobs.
• Finding the quick wins will give you results and help justify engineering spend.

 
DataTick can help you find the right balance

It’s important to know what value different data activities can deliver

• Whilst insights from analytics are important, they are not everything
• You need the full range of data activities to optimise your data value
• The value of data is delivered by dashboards, reports, data science models, data feeds, APIs, Visualiation, alerts or automated triggers
• The deeper, broader and better your data the greater the value you get

DataTick can help you drive the right business case from data for your organisation

Failure is common: It’s important to know how to avoid it

Jan 2019: Gartner says 80% of analytics insights will not deliver business outcomes through 2022 and 80% of AI projects will “remain alchemy, run by wizards” through 2020.

Nov. 2017: Gartner says 60% of #bigdata projects fail to move past preliminary stages. Further analysis showed the numbers were closer to 85%.

Nov. 2017: CIO.com lists 7 sure-fire ways to fail at analytics. “The biggest problem in the analysis process is having no idea what you are looking for in the data,” says Tom Davenport, a senior advisor at Deloitte Analytics.

Data warehouse projects are among the most visible and expensive initiatives an organization can undertake. Sadly, they are also among the most likely to fail. At one time Gartner reported in 2017 that more than 50% of data warehouses would fail to make it to user acceptance. Because of the size of investment (both time and money) required, the success of such a project can make or break careers.

DataTick can help you navigate your data journey enabling you to avoid the pitfalls