Types of Information Systems for Startup Business

For startup companies, the key business functions include: Sales and Marketing, HR, Finance and Accounting, and Manufacturing. Present-day startups require information systems for monitoring all their business operations, such as business planning, material acquisition, production, quality control and delivery to markets. In view of the aforementioned key business roles of startup companies, the chief information systems required are; Marketing and Sales Information Systems, Production and Manufacturing Information Systems, Accounting and Finance Information Systems, Strategic Information Systems, Enterprise Collaboration or Office Automation Systems and HR Information Systems (Al-Mamary, Shamsuddin & Aziati, 2014).

Sales and Marketing Information Systems

Marketing and sales departments are in charge of selling company offerings. Marketing chiefly deals with: determining buyers of the company's services/products, ascertaining their demands and requirements, planning and creating services and products for satisfying their demands, and conducting advertising and promotion activities for these services and products. The sales function deals chiefly with getting in touch with buyers, selling the company's services and products, order-taking, and follow-up sales. The aforementioned functions are supported by marketing and sales information systems. Shim (2000) claims that marketing information systems backs management in the domains of service/product development, pricing, marketing mix, successful promotion, sales forecasts, and distribution.

Finance and Accounting Information Systems

The financial function deals largely with handling corporate financial assets and investments like cash, bonds and stock. The accounts function deals with retaining and handling corporate financial records, including payroll, receipts, and depreciation (Al-Mamary et al., 2014). Shim (2000) asserts that accounting software's central job is automation of the routine activity of accounting transaction entry and posting. This data is electronically organized for developing financial statements. It is also easily accessible for aiding company leaders in managing the company. Finance information systems offer financial data to every financial manager in a company. Financial decisions will normally be made depending on data yielded by accounting systems.

Human Resource Information Systems

HR Information Systems deal with generating, categorizing, maintaining and distributing information on the workforce for aiding the company's managers at different levels to undertake appropriate decision-making. Currently, most successful firms employ HR information systems for facilitating everyday HR operations (Al-Mamary et al., 2014). Corporate HR departments are in charge of drawing in, training and retaining personnel in the company. HR information systems, thus, facilitate tasks like determining prospective employees, having comprehensive files of current workers, and coming up with programs for developing and improving personnel skills and knowledge.

Manufacturing and Production Information Systems

Production and manufacturing functions are in charge of actual creation of services and products. Production and manufacturing systems handle the tasks of: planning, developing, and maintaining production facilities; setting up production objectives; acquiring raw materials, storing them and ensuring they are always at hand for uninterrupted production operations; and scheduling material, equipment, human resources and facilities for fashioning finished goods. The above tasks are supported by production and manufacturing information systems. Rivera and Hernandez (1997) write that production information systems are computer programs capable of handling a production-linked data pool. Shim (2000) maintains that manufacturing information systems' mission is: applying computer technology for improving production effectiveness and processes, thereby improving product quality and decreasing manufacturing expenses. That is, manufacturing systems are systems which take in information systems technologies, material, data, equipment, and management, and employ the information and manufacturing process for generating an output of better end products.

Enterprise Collaboration Systems (Office Automation Systems)

Corporate automation systems make up one among the commonest information system varieties that assist managers in controlling organizational data flow. These systems are in charge of enhancing workgroup and team efficiency and communications. Although operating on a broad level (they aren't specific to organizational levels), these systems offer key support for a wide array of clients. They aim at supporting office activities using information technology. Email, voice mail, video conferencing, multimedia system, group decision-making and file transfers may be carried out using enterprise collaboration systems (Al-Mamary et al., 2014).

Strategic Information Systems

These systems use IT for affording firms a strategic competitive edge through its business processes, products, or services (Nowduri & Al-Dossary, 2012). They represent a special kind of corporate information system whose chief task is securing or retaining a competitive edge.

Efficient and effective acquisition, processing and analysis of operational and big information will aid a firm in acquiring a more comprehensive grasp of its business, clients, offerings, rivals and so forth. This may bring about efficiency enhancements, decreased expenditure, improved sales, improved services/products and improved customer service (McGuire, Manyika & Chui, 2012).

Operational and big datasets help surmount conventional limitations economically, opening avenues for ingesting, storing and processing information from novel sources like market data, social media, communications, and digital interactions with clients. Some figures reveal that over eighty percent of organizational information is not structured and, hence, not appropriate for subjecting to conventional data processing systems (McGuire et al., 2012; LaValle et al., 2011). The use of big data facilitates unstructured data processing and increases system intelligence that may then be utilized for improving sales performance, better understanding client requirements, supporting marketing campaigns, undertaking better fraud monitoring, and reinforcing internal organizational risk management.

Big data competence aids companies in integrating two or more data sources easily and time-effectively. This, together with decreased per-GB storage expenses, helps companies develop, for instance, a federated client view by moving client information from numerous isolated company departments into one structure, and subsequently running consolidated data analytics and reports (LaValle et al., 2011). Further, big data technology releases firms from the historical cost-accuracy challenge, permitting them to retain information at minimal levels of detail, maintaining all information history economically and easily.

The big data concept has given rise to a novel paradigm in data architecture. Earlier, data systems had a fixed collection of information requirements. Now, however, platforms for data storage aren't limited to an inflexible, predetermined data model. Data systems can now manage all sorts of unstructured and structured information (McGuire et al., 2012). Unstructured information assimilation may, particularly, improve reporting and analytics. For instance, the organizational objective of maintaining an integrated customer profile view across company geographies and functions is vital to: Ensuring more intelligent organizational decision-making; Enhancing client profile monitoring to identify "red flags" (i.e., opportunities or threats), and Allowing the firm to provide more appropriate and customized customer services.

Big data utilization is growing into a critical means for ensuring leading organizations outdo rivals. In a majority of sectors, both fresh entrants and established firms take maximum advantage of data-guided strategies for innovating, competing, and capturing value. In fact, initial examples of this kind of data utilization were seen in all studied sectors. In the healthcare domain, information pioneers analyze health consequences of widely-administered/prescribed drugs and identify risks and advantages not apparent in necessarily narrower clinical trials (LaValle et al., 2011). Other front runners in big data adoption utilize information from sensors imbedded in all sorts of products, right from industrial products to toys for ascertaining their offerings' actual real-world application. This knowledge informs novel product/service design and development.

Big data also facilitates creation of novel growth opportunities as well as wholly new company categories, like those aggregating and analyzing industry information. A large number of these firms are in the midst of huge data flows, and capable of capturing and analyzing information regarding services, products, buyer preferences, buyer intent, suppliers, and buyers (McGuire et al., 2012; LaValle et al., 2011). Revolutionary sector-wide managers must commence aggressive efforts to build organizational big data competences.

Besides big data's scale, its high-frequency and real-time characteristics are crucial as well. For instance, 'now casting' or the ability of instantaneously estimating measures like buyer confidence, which was earlier carried out only retrospectively, is growing in popularity, significantly increasing predictive power (LaValle et al., 2011). Likewise, high frequency data enables buyers to try theories out in almost real-time, to previously-impossible levels.

Operational and big data is able to unlock appreciable value by ensuring information transparency. Considerable quantities of facts are still not digitally captured (for instance, hand-written information, or information that can't be easily searched or accessed via networks). Researchers reveal that as much as a fourth of the efforts in certain knowledge workgroups comprise of seeking facts and their subsequent transfer to a different (sometimes even virtual) site (LaValle et al., 2011). These efforts form a key cause of inefficiency.

With companies creating and storing more transactional information digitally, they are able to collect more comprehensive and correct performance data on all aspects including sick days, product inventories, etc., thus improving performance and exposing variability. Some leading companies actually harness their ability of garnering and analyzing big data for carrying out controlled experiments and improving management decision-making (LaValle et al., 2011; McGuire et al., 2012).

Furthermore, big data enables ever-smaller market segmentations and, hence, more accurately tailored services/products.

Complex analytics are able to appreciably improve decisions, reduce risks, and uncover important, otherwise concealed, insights.

Lastly, big data may be utilized for developing next-gen services and products. For example, producers are employing information gleaned via sensors imbedded in goods for developing inventive after-sales services like proactive maintenance, hence, avoiding new product failures (LaValle et al., 2011).

References

Al-Mamary, Y. H., Shamsuddin, A., &Aziati, N. (2014). The Role of Different Types of Information Systems in Business Organizations: A Review. International Journal of Research, 1(7), 333-339.

Hernandez, W., & Rivera, J. M. (1997). A production information system, an application in the pharmaceutical industry. Computers & industrial engineering, 33(1), 15-18.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.

McGuire, T., Manyika, J., & Chui, M. (2012). Why big data is the new competitive advantage. Ivey Business Journal, 76(4), 1-4.

Nowduri, S. & Al-Dossary, S. (2012). Management information systems and its support to sustainable small and medium enterprises. International Journal of Business and Management, 7(19), 125.

Shim, J. K. (2000). Information systems and technology for the noninformation systems executive: an integrated resource management guide for the 21st century. CRC Press.