|Melinda Kenneway: loves data|
‘Data really powers everything that we do.’
If we’re not selling content, what are we selling? Data is also becoming a product or service itself now. An era of big data can help us understand and deliver a much better experience for customers In 2012, 90% of all the data that existed in our entire history has been created in the previous 2 years. We need to get the basics of data right, but also have a strategy.
It’s knowing what you need to know rather than trying to find out about everything. Have clear objectives and stick to them. Don’t forget to consider privacy and legal issues about data. Deliver useful services that build on what the market thinks is acceptable. And if you don’t like data now? You should get out of marketing.
Colin Meddings from DataSalon focused on why you should care about cleaning up your data. To start with it can embarrass or annoy your customers when it is wrong and it’s open to error when users don’t put in info correctly.
|Colin Meddings: not vague about data|
‘Publishers can’t even tell a library what they subscribe to.’
We’re too vague about absolute fundamental data. The basic process of publishing involves: submit, review, copy edit, publish, purchase, read, cite, licence. All of these aspects generate data. The volume of data is huge and all these areas are where errors or mistakes can happen and where data can conflict.
Reflect on your efficiency with dealing in data. Do you struggle with data wrangling where you have to continually review and refresh datasets? See data as an asset in your business. It is worth investing in. You will get a return. Don’t forget that there’s a legal requirement around data to ensure it is accurate and fit for purpose. He recommended the DQM Data Governance Maturity Model. Be aware. Become reactive. Turn this into proactive. Then make it a managed process. And finally, it becomes optimal.
Meddings outlined the seven deadly sins of data quality:
- Missing data
- ‘Siloed’ data
- Invalid data
- Out of date info (people move around)
- Right information, wrong field
- Duplicate and conflicting data.
Put some effort and resource into sorting data. Task to someone specifically, create champions, set targets. It’s great if you can employ a team, but if you can’t, get senior management buy-in and work with those who have aptitude/passion for data. Start with one problem, don’t try to fix all at once.
Do a data audit. It can be manual. People who work with data everyday will know where the problems are. Sometimes an automated audit is better – good at finding information about your data for identifying empty fields, etc.