Machine Learning, AI and Advanced Analytics

By | Database Technology Trends
Big data is big news across industry sectors. The digital age has given rise to information overload, and the most resourceful businesses are learning how to take advantage of that information. It takes a special type of skill to gain actionable insights from these massive amounts of data, and that is where advanced analytics comes in. With technologies like artificial intelligence (AI) and machine learning (ML), data-driven business insights are possible like never before. Here’s a breakdown of how these technologies really work. The Science of Advanced Analytics – Traditional analytics refers to more linear quantitative analysis, usually generated by business intelligence (BI) systems. These are examinations of performance drawn from a structured and pre-defined framework. They use existing historical data from a database or data warehouse. With advanced analytics, we are able to follow the data without a pre-determined scope. All you need is a starting point. For example, consider the question: what type of people buy yogurt in the mornings? With traditional analysis, you would be left skimming through sales data and designing reports to cross reference yogurt sales and time of day, but that only tells you how many people have performed this action, not what type (more…)

Understanding Blockchain, the Trending New Tool

By | Database Technology Trends
There’s been an increasing amount of buzz around blockchain technology lately, and people are more interested than ever before. “A new technology that functions as a distributed ledger within a decentralized network and promises greater security, lower transaction costs, etc.” is usually how it’s described. But what does all of this really mean? What is Blockchain? In its simplest form, Blockchain is a digital ledger (consider the general ledger your organization’s finance/accounting teams use) that stores information across a network of computers. It stores information in batches called “blocks,” that are linked together in a chronological fashion, similar to a chain of blocks. This information can be virtually anything, but try imagining it in terms of a financial transaction, since that’s probably the easiest to visualize. To illustrate, here’s what happens when you make a purchase: Imagine purchasing new software online from Microsoft (for illustrative purposes only). The block would store the date, time, and dollar amount of your purchase. It would also log information about who is making the transaction. For security purposes, your name would be recorded as a unique “digital signature,” more like a username instead of your actual name. Once your block has been verified and (more…)
Database Consulting

What are Data Clusters and What Can They do For You?

By | Azure, SQL Server
Learning to effectively manage large datasets is key to optimizing your data analytics. Using data clusters can help optimize your data management. It involves grouping data points with similar characteristics into the same cluster to speed up processing time and improve analysis. Consider starting an analysis project on customer spending habits by gender in different regions. Your time on the project would be maximized if you’re able to use statistical techniques to automatically organize the data into logical groups prior to analyzing. How do data clusters work? Data clustering allows you to partition large volumes of structured and unstructured data/observations into logical groupings. One way it does this is by analyzing all of the data in the data warehouse and comparing each data point with clusters created. You rely upon the clustering algorithms to sort and cluster the data in a logical way. In a perfect world, all data points in the same group should be highly similar in nature, while data points in different groups should be dissimilar. There are several different models and algorithms that guide the clustering process. Here are a few: Hierarchical Method: This method creates separate successive clusters using specific criteria. Partitioning Method: This method (more…)

3 Top Analytics Tools To Boost Your Productivity

By | Azure, Database Technology Trends, Oracle, SQL Server
Data analytics have become a major focus for companies determined to be competitive in the market. Leaders need to be able to access and analyze timely data quickly to help drive business decisions that will sustain their companies’ competitive advantage. Thankfully, the growing technology market continues to make this possible. Companies already have numerous analytics tools to choose from, and the options will continue to grow. We will now discuss three of the top analytics tools you should consider before making your next strategic move. 1. Microsoft SQL Server Analysis Services (SSAS) – SSAS is an innovative processing and data mining tool within the Microsoft SQL Server. It analyzes large amounts of data, provides business intelligence reports, and can be used to create business models to support business decisions. All data is stored within a data warehouse, and users can access to create cubes that allow faster analysis. These cubes are multi-dimensional and can be viewed in various ways. Imagine the data as a pivot table that can be modified using certain dimensions and attributes. Multi-dimensional expression (MDX) is the language used to query a cube. Compared to other leading analytics tools, Microsoft SSAS is less complex and much easier (more…)

Data Science: Making the Most of your Big Data

By | Azure, Database Technology Trends, Oracle, SQL Server, Sybase
As today’s trends bring more and more data to our fingertips, the next challenge is figuring out what to do with it all. The insights gained from proper data analysis have major implications for business. Proper data analysis takes careful consideration, appropriate infrastructure, and understanding of data science. What is Data Science? This is the process of using data to draw conclusions or predict outcomes. You probably use data science already. For example, a restaurant owner may keep records of which dishes are ordered. Over time, he may notice that chili is ordered more frequently on rainy days than on sunny days. This may encourage him to prepare larger batches of chili when the weather calls for rain. This is a very simple example of data science. You can see how understanding patterns and trends in data can help prepare your business. Data science can contribute to the following business improvements: Streamline Processes – You can use data science to analyze your business processes and identify areas to streamline. Looking at your website usage statistics may show areas where your potential clients are losing focus, or identify slow and frustrating pages in your online ordering forms. Identifying these patterns can (more…)