Companies already know that they need to better leverage internal data like transaction data, customer interactions, Net Promoter Scores(SM) (NPS), and other process and performance metrics. However, it’s no longer enough to rely only on internal data; companies need to supplement their own data with external data like weather, traffic, social media listening, partner data, and economic data from third-party sources.
According to Forrester Analytics Business Technographics Data And Analytics Survey, 2020, 70% of global data and analytics decision-makers report that their firm has implemented, or is implementing an initiative to expand its ability to source external data; a further 17% say their firm plans to do so in the next 12 months.
For this purpose, companies are hiring data hunters. You want the data hunt to be systematic and efficient so that you can easily compare the data sources in the procurement process. In addition, a systematic process enables scaling of the process. But what are the aspects to standardize in the hunt?
What to consider in a data hunt?
First of all, you should be asking what is the business objective for which data will be used? It might be for example customer segmenting, route optimization, process optimization, pricing finetuning, customer-related insights to boost sales.
Secondly, what is the intended data use? How data will help us to gain desired business objective? Are we going to resell the data as well? Are we going to share it across organizations and partners? What is the expected storage length? Minutes? Weeks? Months?
Thirdly, what are the data requirements? How do we prefer a data supply chain to deliver data to us? Is it API, dataset, FTP, visualization, dashboard? How do we handle normalization and integration? What are the requirements regarding data frequency? Realtime? Batch?
Fourthly, what are the expected outcomes and how do we measure success? Buying data is an investment and thus we need to measure the impact to see whether it was or is a good investment. My message to you all is that measure, measure and measure. Think how you prove with DATA that you made a good judgment call on purchasing this data.
Sixthly, what are desired terms and conditions to meet. Are there any special terms and conditions we expect and consider a must have? What is the estimated cost for the purchase?
Finally, what are the sources to find desired data? List of data markets and other means to find desired data. Another option is to find desired data from some larger data pool by mining. Listing those as candidate sources might become handy as well.
Candidate addition to the Data Product Toolkit
The more detailed checklist for data hunters is a strong candidate to be added to the Data Product Toolkit. Check out the existing value enabling set of business tools and let us know when you start making use of it?
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