How Data Challenges Organizations
How Data Challenges Organizations
Read this article in order to gain more knowledge on how data and insufficient software infrastructure challenges organizations.
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Businesses need to understand what is currently happening with their customers and processes to predict what will happen next and to find actionable insights across all of their data. This process is broken today in many companies — kept in a dysfunctional state by insufficient technical infrastructure as well as organizational hurdles.
Employees’ Bad Habits Can Be Caused by Insufficient Software Infrastructure
It is common for employees to collect data in flat files, spreadsheets, or, in the best case scenario, in individual databases instead of using the far more powerful data tools available. If as few as fifteen analysts add data to seven or eight spreadsheets a day, the situation can spiral out of control pretty quickly. How can anyone expect to draw collective insights from such a scattered amalgam of information? Not to mention, it’s nearly impossible to comply with regulatory initiatives with data in such a state.
There Are Organizational Challenges Related to Collecting Data
In addition to these technical problems, there can be organizational challenges to collecting data. That is, data can often be locked in bureaucratic struggles. To get what they need, analysts may need to beg multiple people and wait. So what happens when a slightly different data set is needed for analysis? It is not quickly attainable. Even if analysts manage to gain the data they need, once it is presented, managers from different departments may argue over its accuracy — particularly if its source is secret or is otherwise noncanonical.
A Possible Solution Exists
Clearly, what is needed is a central repository of data that is agreed upon by the organization to be the single source of truth. A proven solution in this situation is a data warehouse (DWH). Data from the necessary sources can be regularly transferred to the DWH and maintained in a standard format so that models can be trained and business intelligence tools can be utilized. The additional benefits of having a completely separate repository for analytical data include removing analytical load from operational databases and having the ability to collect far more data than would be otherwise possible.
There Are Challenges Related to the Solution
There are several challenges related to establishing a DWH in any organization. To begin with, setting up ETL for a DWH can be time consuming and expensive. These ETL costs are usually added on top of the operating costs of the DWH. Furthermore, DWHs need to be properly designed to accommodate future data growth. This requires a DWH solution provider that has the appropriate expertise. Finally, real-time analytics is a common requirement in today’s fast business environment. Not all DWHs have a great solution for real-time operations.
TaranHouse as an Option
TaranHouse, an economical solution intended for businesses that are just starting to feel “relationally limited,” overcomes the challenges mentioned above: it includes free ETL setup with each of its annual packages, its team has deep expertise in designing data warehouses, and it has a real-time component powered by Tarantool — the accelerated in-memory database.
TaranHouse can reside in a cloud of your choosing, on-prem, or a combination of the two. With TaranHouse cloud, your data is safe as all data is replicated to three data centers and any node failure triggers an automatic failover. TaranHouse can be infinitely horizontally scaled on commodity servers for significant cost savings, and it offers many user-selected parameters such as the ability to choose between row and column-based configurations. Finally, BI tools can be connected to TaranHouse through its ODBC, JDBC, Python, and R connectors.
For a free cloud trial, visit www.taranhouse.com.
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