Dataception

At the most basic level, data deduplication refers to the deletion and removal of redundant or duplicate data. It is an ongoing process to ensure no excess data is in your database, and that you’re using only a single copy of truth, or the golden record, for analytics or operations. Redundant or duplicate data can harm your business and your strategy in many ways, both in operational use cases and analytical use cases. From an operational perspective, you can’t answer questions like which account is the right one to contact? From an analytics perspective, it’s hard to answer questions like who are my top paying customers by revenue? Data deduplication has a lot of overlap with data unification, where the task is to ingest data from multiple systems and clean it. It also overlaps with entity resolution, where the task is to identify the same entity across different data sources and data formats. What are the benefits of deduplication? Data deduplication can benefit your business in a my...

We’ve all been collecting customer data for years. But here’s the hard truth: Data is meaningless without a comprehensive, organization-wide strategy to consistently leverage it into actionable insights. While we might know what our customers are buying and how they’re buying it, without connecting the dots to understand customers’ path to purchase, we can’t be sure how to engage them to purchase again. It’s data’s potential to move beyond the transaction and demystify the full customer journey that makes it extraordinarily valuable – and then, only if we can leverage it. A solid and prescriptive data strategy can deliver the following business benefits: Increase revenue and drive down costs Solve complex business problems Drive customer insights and intimacy Lower business risks by predicting trends and customer behaviors Crystallize previously “invisible” emerging opportunities Support overall business strategy Data Strategy Fuels Business Outcomes It’s a compelling fact that is supp...

Data Strategy describes a “set of choices and decisions that together, chart a high-level course of action to achieve high-level goals,” according to DAMA’s Data Management Body of Knowledge (DMBoK). This approach includes business plans to use data for a competitive advantage and support enterprise goals. Having such a blueprint to align decision-making and activities around data assets makes it invaluable. Data Strategy requires an understanding of the data needs inherent in the Business Strategy. According to Donna Burbank: “It’s the opportunity to take your existing product line and market it better, develop it better, use it to improve customer service, or to get a 360-degree understanding of your customer. Data Strategy is driven by your organization’s overall Business Strategy and business model.” A Well-Developed Data Strategy Has: A strong Data Management vision A strong business case/reason Guiding principles, values, and management perspectives Well-considered goals for the ...

How organizations manage their data directly impacts their success or failure. The correlation between data analytics and intelligence to competitive advantage and growth has led to heavy investments in those technologies throughout the last decade. So, if you consider that content is the consumable form of data, then it follows that the era of big data has now given way to the era of big content. Employees, customers, partners, investors, and regulators – all internal and external stakeholders – are clamoring for content to stay employed, educated, entertained, and connected. And all these content consumers are more empowered than ever before, meaning organizations must harness not just the power of their data but also that of their content assets to meet information demands. This data-content continuum exists because of the inherent challenges and opportunities both data and content management share and because content is the form of data closest to your customers and other key audie...

The effects of the pandemic have popularized a unique work style that places a higher value on providing employees with the options of either a hybrid or a fully remote work environment for many employees. In fact, 81% of people surveyed believe hybrid work will be the foremost working model by 2024, with 56% of work being done offsite, according to AT&T’s 2022 Future of Work Report. Consequently, workers and their technology will be conducting business in a fully remote or at least hybrid IT model, which causes data security concerns for technology assets. Considering that 72% of companies lack effective hybrid work strategies, this leaves organizations scrambling to efficiently manage their ever-growing IT infrastructure and technology assets. To help mitigate concerns and better manage their IT estate, organizations can implement 10 enterprise technology management (ETM) practices that will increase IT infrastructure visibility and better prepare companies for a hybrid or fully ...

If your business is struggling to forecast and predict outcomes and results, your management team is probably considering predictive analytics. The technology research firm Gartner states that “by 2025, 50% of data scientist activities will be automated by artificial intelligence, easing the acute talent shortage.” For the average team member, the concept of predictive analytics may seem daunting, and if you are a business user whose management team has asked you to embrace and participate in analytics, the addition of predictive analytics to your day-to-day business processes may seem irrelevant, or it may seem to mean you will be expected to work harder or produce more output. But don’t be too quick to assume the worst.  Let’s take a look at predictive analytics, the benefits of assisted predictive modeling and its importance in the organization, and how intuitive augmented analytics can help business users achieve their goals without requiring advanced training or additional wor...

Businesses that realize the value of their data and make the effort to utilize it to its greatest potential are quickly outcompeting those that do not. But like any complex system, the architectures that utilize big data must be carefully managed and supported to produce optimal outcomes. One of the chief obstacles in this continual process is the isolated nature of many data environments these days. Whether in the data center, colocation facilities, or the cloud, data silos prevent the kind of data integration that is needed to excel in a digital economy. This isolation is not only physical, but categorical as well. Multiple processes tend to create volumes of increasingly diverse data sets – such as sales data, finance data, even maintenance records – that at first glance might not seem to influence each other but in fact provide broader views of the truth when analyzed in tandem. The value of data, after all, is enhanced primarily through its relationship and influence on other data...