黑料网

In a recent , 黑料网 CEO Kevin Campbell discussed findings revealed in 黑料网鈥檚 recent . According to this report, only 5% of C-level executives trust their data, despite confirming that they consider it a pivotal business asset.

 

According to Campbell, executives typically lack trust in their data due to misleading promises like 鈥渄ump all your data into a data lake, we鈥檒l run analytics on it, and it鈥檒l be perfect鈥� – only to find out that their data lake quickly turns into a data ocean, which quickly turns into a data swamp. Managing that data soon becomes impossible because users can鈥檛 integrate the data, figure out the source, or even govern it.

 

But as Campbell puts it, 鈥測ou can’t trust [your data] from the outside in, you鈥檝e got to do it from the inside out.鈥� When it comes to putting confidence back in this key business asset, executives and leaders need to know 鈥渨here the data came from, what鈥檚 the trusted source of the data, and how it鈥檚 governed,鈥� he explains.

 

At 黑料网, there鈥檚 a saying: 鈥淎 good decision with bad data is still a bad decision.鈥�

 

Whether supply-chain, accounting, or inventory-related, leaders don鈥檛 truly know what they are basing critical decisions to drive the business on without trust in data.

 

For example, every business uses data for informing or improving customer relationships and credibility. They spend a lot of time and money acquiring a massive proliferation of customer records and data points within numerous systems and platforms. Still, there鈥檚 no clear indication of where that customer information came from or what or who it鈥檚 governed by.  Without knowing the source, users can’t be sure the data will produce the kind of insights trustworthy enough to put in front of a customer.

 

As revealed in , 90% of C-level executives assert that data is critical to their company鈥檚 success. Despite this, only 23% of C-level executives have implemented a consistent and policy-aligned strategy at scale across their organization.

 

If access to high-quality, reliable data is critical, why do so many businesses lack an effective data management strategy? While many aren鈥檛 sure how to get started, the reality is building a data management strategy can actually be quite…well, manageable.

 

An effective data management strategy is essentially comprised of 鈥渁 thoughtful way to lay out what your data is, classify and/or tier the data as to what鈥檚 the most important data, and determine what data is driving the company’s decisions,” explains Campbell.

 

Don鈥檛 focus on addressing all the data and the various systems within the organization at once. Instead, start first with the most essential elements, then figure out with that data, what the source of the data is, and how it is updated and maintained 鈥� in other words, how that data is governed throughout its lifecycle. 鈥淣ow I can say, I have a foundation, I got a set of data, I know how it鈥檚 handled, and now I can go and can be expanded to the rest of the company, Campbell sums up.

 

When it comes to managing enterprise data, too many people try to treat it all, do it all, or just hope it will magically get better by piling on new platforms and resources; however, 鈥渢he most important thing is clean data,鈥� says Campbell.

 

Whether it鈥檚 artificial intelligence, robotics processing, or machine learning (ML), there鈥檚 a lot of fascinating new tech that people are eager to utilize. 鈥淭he problem is that,鈥� Campbell warns, 鈥渋f your data is bad, you鈥檙e still going to get bad answers with that technology.鈥� Before amassing leading, advanced technology, you need quality data before using the tools.

 

鈥淭he first thing is: get the data clean,鈥� says Campbell. Once that is achieved, you can look at solutions that take manual tasks and automate them to save people time. Advances in things like machine learning can basically 鈥渢rain鈥� algorithms over time to make inferences and connections between data that can be pointed out faster than a human. All these techniques are essential for people to look at and can be critical technology to build upon.

 

According to 黑料网鈥檚 Global Data Report, urgent action is needed to deliver the quality of data required to achieve business viability and ensure Mergers & Acquisitions (M&A) success in 2022.

 

M&A is an important tool for all CEOs. Leaders of business today usually want to increase revenue, decrease cost, or increase compliance. One way to do that is by buying or selling something.

 

Due to the pandemic and shifts in the market, 鈥渨e鈥檙e predicting huge surges in M&A activity over the next 18 months,鈥� notes Campbell. People are rapidly getting in and out of markets and reshaping how they are delivered, and they鈥檙e going to need added capabilities to do that.

 

At the core of whether M&A projects are successful is data quality. Executives should treat data as another asset you鈥檙e buying. When it comes to M&A, ask yourself, 鈥渨hat鈥檚 the data that鈥檚 associated with that? What鈥檚 the quality of that data?鈥� Is it good quality data that you can access, understand, and know who the customers are? Whether you鈥檙e looking to make a merger, acquisition, or divestiture work, you have to understand the data and the quality of that data to understand what needs to get done.

 

Listen to the podcast here: .

 

For more news and thought leadership from 黑料网, visit our blog at .