A few years ago, the predictions for the following years show us a world in which we were going to be overwhelmed by information and would need to be prepared to process and take advantage of that new reality. No longer enough to process information from business systems without knowing the context. Call it what you want, but this enrichment of the information is already in our phones, in our homes, in our cars, in our social networks, and entire cities! Today, those who do not adapt to that change could not survive.
At Bix, we are committed to bringing this change to those organizations that need manage and process the increasing information in terms of variety, velocity, and volume. No mater if you already have or not a platform that supports traditional BI or the new wave of BIG DATA needs, we can help to integrate, transform, aggregate, and present all relevant information in analytics ways to do more clear, simpler, and faster the business decisions processes.
The digital universe is doubling in size every two years, and by 2020 it will reach 44 zettabytes (44 trillion gigabytes). - IDC
Discover the power of your business data and its environment
Know the most influential variables in your business results
Recognize the advantage of having the right information at the right time
Be aware of the competition and anticipate their movements
Some Business Use Cases By Industries
Telecommunications operators need to build detailed customer churn models that include social media and transaction data, such as CDRs, to keep up with the competition. The value of the churn models depends on the quality of customer attributes (customer master data such as date of birth, gender, location, and income) and the social behavior of customers. Telecommunications providers who implement a predictive analytics strategy can manage and predict churn by analyzing the calling patterns of subscribers.
Generally associated with financial, fraud management predicts the likelihood that a given transaction or customer account is experiencing fraud. Solutions analyze transactions in real time and generate recommendations for immediate action, which is critical to stopping third-party fraud, first-party fraud, and deliberate misuse of account privileges. Solutions are typically designed to detect and prevent myriad fraud and risk types across multiple industries, including:
- Credit and debit payment card fraud
- Deposit account fraud
- Technical fraud
- Bad debt
- Healthcare fraud
- Medicaid and Medicare fraud
- Property and casualty insurance fraud
- Worker compensation fraud
- Insurance fraud
- Telecommunications fraud
Marketing departments use Twitter feeds to conduct sentiment analysis to determine what users are saying about the company and its products or services, especially after a new product or release is launched. Customer sentiment must be integrated with customer profile data to derive meaningful results. Customer feedback may vary according to customer demographics.
Retailers can target customers with specific promotions and coupons based location data. Solutions are typically designed to detect a user's location upon entry to a store or through GPS. Location data combined with customer preference data from social networks enable retailers to target online and in-store marketing campaigns based on buying history. Notifications are delivered through mobile applications, SMS, and email.
IT departments are turning to big data solutions to analyze application logs to gain insight that can improve system performance. Log files from various application vendors are in different formats; they must be standardized before IT departments can use them.
Retailers can use facial recognition technology in combination with a photo from social media to make personalized offers to customers based on buying behavior and location. This capability could have a tremendous impact on retailers? loyalty programs, but it has serious privacy ramifications. Retailers would need to make the appropriate privacy disclosures before implementing these applications.
Utility companies have rolled out smart meters to measure the consumption of water, gas, and electricity at regular intervals of one hour or less. These smart meters generate huge volumes of interval data that needs to be analyzed. To gain operating efficiency, the company must monitor the data delivered by the sensor. A big data solution can analyze power generation (supply) and power consumption (demand) data using smart meters.
How Can We Help You?
Choosing what data is potentially valuable is a difficult task, but first at all, you should think about understanding the company goals. Then, you could start to think about what information do you need to help to accomplish the company goals. Thus, you should visualize all the possibles business cases to use this information and then classify it into types of solutions.
Depending on the type of project, architectural needs are quite different. Depend of your needs, can be better a Lamda Architecture or not. For example, they have almost nothing in common a batch project than a real time project, in terms of infraestructure, device types, interfaces, tools, monitoring issues, etc.
We are convinced that a project should be planned and leading with suitable methodologies and tools. We mostly work with PMI and Scrum best practices, but we are flexibles enough to keep in mind work based in results.
The types of data to be processed are increasingly diverse and interfaces with the sources are increasingly specific. Establish those ties can be a daunting task in a growing universe of tools.
There are more and more data to analize every day. Integration processes like ETL (Extracting, Transforming and Loading Data) are the alchemism of data. We help to manage the growing integrations between disparate systems, data cleaning, logical transformations, data aggregation and loading in destiny.
All the information should be store in a unique repository that meet the needs of the business analysis. It should be robbust enogh to scale but flexible enough to adapt to changes.
Applying many kind of preddicting models with computing algoritms, we could discover data patterns like repetitive behavioral conducts or identify segments of data that can be clasiffied on new ways.
Machine Learning will allow you to generate automating programs based on statistical analysis and predict events to will happen. Thus, the program learns with a critical mass of data, processing a huge ammount of variables that the humans can't on the needed time.
The most important issue to the consumer of data is the way to understand them. Is not the same doing analysis on data grids than doing analysis on graphs. We take this task carefuly thinking in the best way to show the KPIs.
Do you find these questions familiar?
Do I have to focus on clients with an income less than $10k, or $15k? How different is it?
What was the improvement after a marketing campaign over the investment?
Who are the customers with more risk of leaving the service in the next month?
What is the sales forecasting for the next 3 months? How accurate is it?
Common Questions To Answer?
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