Four-step data supply chain

Read time 3min 00sec

When business intelligence (BI) and big data management are done right, companies can literally change their cultures, empowering employees and improving decision-making.

These are the sentiments of Chester Liu, director of platform product marketing at QlikTech, who sat down with ITWeb on the sidelines of ITWeb's Business Intelligence Summit.

According to Liu, the pace of business is constantly increasing and data requirements are changing at an equally alarming rate. He described the animals he saw while on safari in SA as being much more efficient than business. "One of the key differentiators between nature and business is agility," he said. "Business needs to be about how to make the best decision, using the required data."

The self-confessed storyteller used TV weather reports as a theme throughout his address, describing this presentation of high and low temperatures as big data at work. "It is important to figure out how to tell stories with the data you have so that other people can make better decisions."

Liu unpacked what he termed a 'data supply chain', stressing what companies need to think about when collecting data. The chain features four steps, all of which Liu says are necessary in order for data to add value to an organisation.

The first step is data sourcing. "You need to think about what data you need to collect in order to make your business decisions," he said, recommending decision-makers consider business goals when collecting data to ensure they don't waste time and money collecting and storing unnecessary information.

The data enrichment step involves giving the raw data meaning. "This step is very important," he said, adding that raw data lacks the information necessary for someone to gain value from it. During this step, data scientists also make the data relevant. "Relevance in terms of data analytics is so important. Without relevance, you are just swimming in data and you will literally drown."

Equally as important, according to Liu, is the data quality step, which involves having the right tools to quickly detect data errors and address these problems. Companies that lack the tools to catch an error easily could land in trouble, he said.

According to Liu, the final step in the supply chain is data delivery. When delivering data to the desired audience, business must ensure the data is interactive enough for people to ask pertinent questions. "How you present the data is as important as who you are presenting it to," he said.

The value of following this kind of data supply chain comes from answering intended and unintended questions, he said, adding that, sometimes, one gets the most value from the unexpected questions. He stressed the importance of changing thinking within the organisation. Often, too many reports and charts are produced that support the business' own hypothesis, he said; these do not challenge the company's thinking.

"If you get these steps right, there will be a transformation in the role of IT. Once you get your data supply chain down pat, IT is able to add value by being a storekeeper, not a gatekeeper," he concluded.

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