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 workloa

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

  Data is considered an asset. Data is been re-named into “ New-Oil ” of business houses. Data is the heartbeat of today’s business enterprises. We at Dataception , evaluate the real time fact on data and analytics use cases, features and functionalities that support business needs, as well as underlying data management and governance technologies that assist those use cases. We also checked the real time implementation hurdles. Out of our evaluation we think the data can either be a boon or a curse! The same asset- “data”- is holding back the potential innovation and process automation. Here is how and what is our frame work to overcome the hurdles. # Collection of Meaningful Data: There is huge volume of data. Every single transaction generates volume of data and the employees are often overwhelmed by this and they hate data! There are various channels producing data in various forms – structured and un-structured. To collate and create a meaningful data set is posing big

What Is Data Preparation? Data preparation is the process of collecting, cleaning, and consolidating data into one file or data table, primarily for use in analysis The Key Steps to Data Preparation Access Data Access data from any source – no matter the origin, format or narrative. Dataception excels at intelligently and automatically extracting data from complex unstructured and semi-structured sources, like PDFs. Increased access to data means less manual work, faster insights and faster time to value realized by your organization. Cleaning Data and Improving Data Quality Manual data prep is error-prone, time-consuming and costly. Business decisions rely on analytics. But, if the data is inaccurate or incomplete, your analytics inform wrong businesses decisions. Bad analytics means poor business decisions. Dataception is programmed with over 80 pre-built data preparation functions to speed up arduous data cleansing projects. Blending and Reconciling Data Multiple, disconnected syste

Data and analytics are now gaining super power.  The New Normal demands the change in doing business across all sectors.  Especially in manufacturing, retail and health care are most demanding sectors that adopt this new wave.  Data is an asset .  When the enterprises optimizes the data and they are getting the ability to make faster and better decisions. They can save money by working more efficiently. Sometimes they may find new sources of revenue. They can capitalize on the untapped business intelligence that they own already. If at all the business houses, having the advanced business analytics capability that can predict everything, everywhere then they can make their business very profitable. If at all they can predict the customer behavior, they can interact with them more effectively.  Every part of the supply chain can be well oiled, when they are able to understand the need and change in demand. They can make their financial decision from anywhere in the world. This is not m

  Data Modeling techniques are used to create a map or blueprint showing how an organization gathers and processes data. Data models help to define the logical structure for an organization’s data. Data Modeling techniques are necessary for businesses wanting to maximize and streamline their ability to analyze and understand data. While developing the model, the data modeler will work with the business’s data and marketing team to determine the business’s needs. It can take time to develop an effective Data Modeling program. However, from a big picture perspective, it is worth it, because it can save a significant amount of labor and money by finding errors before they occur. Many modern data models include the use of automation and visual interfaces (this allows the user to directly manipulate graphic data on the screen). Data Modeling begins with collecting information about the organization’s business requirements and how the data will be used. This information is used to establish

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. You might not even make it out of the starting gate. So, what can you do to ensure your data is up to par and you’ve achieved data trustability?  Data lakes, data pipelines, and data warehouses have become core to the modern enterprise. Operationalizing these data stores requires observability to ensure they are running as expected and meeting performance goals. Once observability has been achieved, how can we be confident that the data within is trustworthy? Does data quality provide actionable answers? Data observability has been all the rage in data management circles for a few years. What is data observability? It’s a question that more and more businesses are asking as they strive to become more data-driven. Simply put, data observability is the ability to easily see and understand how data is flowing through your system; it’s th